Time Series EDA & Forecasting
# Find data from file for first combination
all_dates = data.frame(date=c("1/1/2013","2/1/2013","3/1/2013","4/1/2013","5/1/2013","6/1/2013","7/1/2013","8/1/2013","9/1/2013","10/1/2013","11/1/2013","12/1/2013",
"1/1/2014","2/1/2014","3/1/2014","4/1/2014","5/1/2014","6/1/2014","7/1/2014","8/1/2014","9/1/2014","10/1/2014","11/1/2014","12/1/2014",
"1/1/2015","2/1/2015","3/1/2015","4/1/2015","5/1/2015","6/1/2015","7/1/2015","8/1/2015","9/1/2015","10/1/2015","11/1/2015","12/1/2015",
"1/1/2016","2/1/2016","3/1/2016","4/1/2016","5/1/2016","6/1/2016","7/1/2016","8/1/2016","9/1/2016","10/1/2016","11/1/2016","12/1/2016",
"1/1/2017","2/1/2017","3/1/2017","4/1/2017","5/1/2017","6/1/2017","7/1/2017","8/1/2017","9/1/2017","10/1/2017","11/1/2017","12/1/2017",
"1/1/2018","2/1/2018","3/1/2018","4/1/2018","5/1/2018","6/1/2018","7/1/2018","8/1/2018","9/1/2018","10/1/2018","11/1/2018","12/1/2018",
"1/1/2019","2/1/2019","3/1/2019","4/1/2019","5/1/2019","6/1/2019","7/1/2019","8/1/2019","9/1/2019","10/1/2019","11/1/2019","12/1/2019"))
date_combinations = merge(all_dates,sample_combinations,all=TRUE)
str(date_combinations)
'data.frame': 840 obs. of 4 variables:
$ date : Factor w/ 84 levels "1/1/2013","1/1/2014",..: 1 29 36 43 50 57 64 71 78 8 ...
$ Product_Type: Factor w/ 57 levels "ABSINTHE","AMARETTO",..: 53 53 53 53 53 53 53 53 53 53 ...
$ Product : Factor w/ 4017 levels "1800 ANEJO TEQ 6PK 750M",..: 1144 1144 1144 1144 1144 1144 1144 1144 1144 1144 ...
$ Customer_ID : int 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 ...
'data.frame': 840 obs. of 6 variables:
$ date : chr "1/1/2013" "2/1/2013" "3/1/2013" "4/1/2013" ...
$ Product_Type: Factor w/ 57 levels "ABSINTHE","AMARETTO",..: 53 53 53 53 53 53 53 53 53 53 ...
$ Product : Factor w/ 4017 levels "1800 ANEJO TEQ 6PK 750M",..: 1144 1144 1144 1144 1144 1144 1144 1144 1144 1144 ...
$ Customer_ID : int 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 ...
$ STD_Cases : num 3 0 1 5 0 0 0 4 0 0 ...
$ Dollar_Sales: num 582 0 206 970 0 0 0 814 0 0 ...
#RF function
library(ranger)
library(randomForest)
library(caret)
rf_ts <- function(n_ahead, target_df, show_graph){
target_df$date <- as.Date(target_df$date, '%m/%d/%Y')
tlen = length(target_df$STD_Cases)
sample_train <- target_df[1:(tlen-n_ahead), ]
sample_test <- target_df[(tlen-n_ahead +1):tlen, ]
sample_test$Dollar_Sales[is.na(sample_test$Dollar_Sales)] <- 0
rf1 <- randomForest(STD_Cases ~date + Dollar_Sales, data = sample_train, na.action = na.exclude)
pred1 = predict(rf1, newdata = sample_test)
rf_ase <- mean((sample_test$STD_Cases - pred1)^2)
predictions <- data.frame(date = sample_test$date, forecast = pred1)
results = c(predictions, data.frame(ASE = rf_ase))
g1 <- ggplot() +
geom_line(aes(x = sort(target_df$date), y = target_df$STD_Cases), col = 'black') +
geom_line(aes(x = sort(target_df$date), y = c(rep(NA, (tlen-n_ahead)), array(pred1))), col = 'red') +
ggtitle(paste('Random Forest', n_ahead, 'Month ASE: ', rf_ase))
if(show_graph == TRUE){
show(g1)
}
return(results)
}
#nn complete
nnc <- function(tvector, n_ahead, reps, hd_vector, display){
#date_line <- as.Date(target_df$date, '%m/%d/%Y') #add date back in after it works
tlen <- length(tvector)
sample_train <- temp1[1:(tlen-n_ahead), ]
sample_test <- temp1[(tlen-n_ahead +1):tlen, ]
ts_train<- as.ts(sample_train$STD_Cases)
fit_mlp <- mlp(ts_train, reps = reps, hd = hd_vector)
mlp_pred <- forecast(fit_mlp, h = n_ahead)
mlp_ase <- mean((sample_test$STD_Cases - mlp_pred$mean)^2)
#r1 <- ggplot() +
# geom_line(aes(x = sort(target_df$date), y = target_df$STD_Cases), col = 'black') +
# geom_line(aes(x = sort(target_df$date), y = c(rep(NA, (tlen-n_ahead)), mlp_pred$mean)), col = 'red') +
# ggtitle(paste('MLP', n_ahead, 'Month ASE: ', mlp_ase), subtitle =paste(target_df$Product[1], 'Purchases By Cust ID: ', target_df$Customer_ID[1]))
#if(display == TRUE){
# print(fit_mlp)
# plot(fit_mlp)
# show(r1)}
forecasts <- data.frame(forecast = mlp_pred$mean)
return(c(forecasts, mlp_ase))
}
# this code block is for individual product/customer combinations, no aggregation is done here
results <- data.frame(Product_Type=integer(),
Product=character(),
Customer=integer(),
ljung_10=double(),
ljung_24=double(),
ljung_results=character(),
top_5_bic=character(),
ADF=double(),
KPSS=double(),
stationarity_results=character(),
EqualMeans_1_ASE=double(),
EqualMeans_2_ASE=double(),
EqualMeans_3_ASE=double(),
EqualMeans_4_ASE=double(),
EqualMeans_5_ASE=double(),
EqualMeans_6_ASE=double(),
EqualMeans_7_ASE=double(),
EqualMeans_8_ASE=double(),
EqualMeans_9_ASE=double(),
EqualMeans_10_ASE=double(),
EqualMeans_11_ASE=double(),
EqualMeans_12_ASE=double(),
EqualMeans_F1=double(),
EqualMeans_F2=double(),
EqualMeans_F3=double(),
EqualMeans_F4=double(),
EqualMeans_F5=double(),
EqualMeans_F6=double(),
EqualMeans_F7=double(),
EqualMeans_F8=double(),
EqualMeans_F9=double(),
EqualMeans_F10=double(),
EqualMeans_F11=double(),
EqualMeans_F12=double(),
AR_1_ASE=double(),
AR_2_ASE=double(),
AR_3_ASE=double(),
AR_4_ASE=double(),
AR_5_ASE=double(),
AR_6_ASE=double(),
AR_7_ASE=double(),
AR_8_ASE=double(),
AR_9_ASE=double(),
AR_10_ASE=double(),
AR_11_ASE=double(),
AR_12_ASE=double(),
AR_F1=double(),
AR_F2=double(),
AR_F3=double(),
AR_F4=double(),
AR_F5=double(),
AR_F6=double(),
AR_F7=double(),
AR_F8=double(),
AR_F9=double(),
AR_F10=double(),
AR_F11=double(),
AR_F12=double(),
ARMA_1_ASE=double(),
ARMA_2_ASE=double(),
ARMA_3_ASE=double(),
ARMA_4_ASE=double(),
ARMA_5_ASE=double(),
ARMA_6_ASE=double(),
ARMA_7_ASE=double(),
ARMA_8_ASE=double(),
ARMA_9_ASE=double(),
ARMA_10_ASE=double(),
ARMA_11_ASE=double(),
ARMA_12_ASE=double(),
ARMA_F1=double(),
ARMA_F2=double(),
ARMA_F3=double(),
ARMA_F4=double(),
ARMA_F5=double(),
ARMA_F6=double(),
ARMA_F7=double(),
ARMA_F8=double(),
ARMA_F9=double(),
ARMA_F10=double(),
ARMA_F11=double(),
ARMA_F12=double(),
ARI_1_ASE=double(),
ARI_2_ASE=double(),
ARI_3_ASE=double(),
ARI_4_ASE=double(),
ARI_5_ASE=double(),
ARI_6_ASE=double(),
ARI_7_ASE=double(),
ARI_8_ASE=double(),
ARI_9_ASE=double(),
ARI_10_ASE=double(),
ARI_11_ASE=double(),
ARI_12_ASE=double(),
ARI_F1=double(),
ARI_F2=double(),
ARI_F3=double(),
ARI_F4=double(),
ARI_F5=double(),
ARI_F6=double(),
ARI_F7=double(),
ARI_F8=double(),
ARI_F9=double(),
ARI_F10=double(),
ARI_F11=double(),
ARI_F12=double(),
ARIMA_1_ASE=double(),
ARIMA_2_ASE=double(),
ARIMA_3_ASE=double(),
ARIMA_4_ASE=double(),
ARIMA_5_ASE=double(),
ARIMA_6_ASE=double(),
ARIMA_7_ASE=double(),
ARIMA_8_ASE=double(),
ARIMA_9_ASE=double(),
ARIMA_10_ASE=double(),
ARIMA_11_ASE=double(),
ARIMA_12_ASE=double(),
ARIMA_F1=double(),
ARIMA_F2=double(),
ARIMA_F3=double(),
ARIMA_F4=double(),
ARIMA_F5=double(),
ARIMA_F6=double(),
ARIMA_F7=double(),
ARIMA_F8=double(),
ARIMA_F9=double(),
ARIMA_F10=double(),
ARIMA_F11=double(),
ARIMA_F12=double(),
ARI_S12_1_ASE=double(),
ARI_S12_2_ASE=double(),
ARI_S12_3_ASE=double(),
ARI_S12_4_ASE=double(),
ARI_S12_5_ASE=double(),
ARI_S12_6_ASE=double(),
ARI_S12_7_ASE=double(),
ARI_S12_8_ASE=double(),
ARI_S12_9_ASE=double(),
ARI_S12_10_ASE=double(),
ARI_S12_11_ASE=double(),
ARI_S12_12_ASE=double(),
ARI_S12_F1=double(),
ARI_S12_F2=double(),
ARI_S12_F3=double(),
ARI_S12_F4=double(),
ARI_S12_F5=double(),
ARI_S12_F6=double(),
ARI_S12_F7=double(),
ARI_S12_F8=double(),
ARI_S12_F9=double(),
ARI_S12_F10=double(),
ARI_S12_F11=double(),
ARI_S12_F12=double(),
ARIMA_S12_1_ASE=double(),
ARIMA_S12_2_ASE=double(),
ARIMA_S12_3_ASE=double(),
ARIMA_S12_4_ASE=double(),
ARIMA_S12_5_ASE=double(),
ARIMA_S12_6_ASE=double(),
ARIMA_S12_7_ASE=double(),
ARIMA_S12_8_ASE=double(),
ARIMA_S12_9_ASE=double(),
ARIMA_S12_10_ASE=double(),
ARIMA_S12_11_ASE=double(),
ARIMA_S12_12_ASE=double(),
ARIMA_S12_F1=double(),
ARIMA_S12_F2=double(),
ARIMA_S12_F3=double(),
ARIMA_S12_F4=double(),
ARIMA_S12_F5=double(),
ARIMA_S12_F6=double(),
ARIMA_S12_F7=double(),
ARIMA_S12_F8=double(),
ARIMA_S12_F9=double(),
ARIMA_S12_F10=double(),
ARIMA_S12_F11=double(),
ARIMA_S12_F12=double(),
RF_1_ASE=double(),
RF_2_ASE=double(),
RF_3_ASE=double(),
RF_4_ASE=double(),
RF_5_ASE=double(),
RF_6_ASE=double(),
RF_7_ASE=double(),
RF_8_ASE=double(),
RF_9_ASE=double(),
RF_10_ASE=double(),
RF_11_ASE=double(),
RF_12_ASE=double(),
RF_F1=double(),
RF_F2=double(),
RF_F3=double(),
RF_F4=double(),
RF_F5=double(),
RF_F6=double(),
RF_F7=double(),
RF_F8=double(),
RF_F9=double(),
RF_F10=double(),
RF_F11=double(),
RF_F12=double(),
MLP_1_ASE=double(),
MLP_2_ASE=double(),
MLP_3_ASE=double(),
MLP_4_ASE=double(),
MLP_5_ASE=double(),
MLP_6_ASE=double(),
MLP_7_ASE=double(),
MLP_8_ASE=double(),
MLP_9_ASE=double(),
MLP_10_ASE=double(),
MLP_11_ASE=double(),
MLP_12_ASE=double(),
MLP_F1=double(),
MLP_F2=double(),
MLP_F3=double(),
MLP_F4=double(),
MLP_F5=double(),
MLP_F6=double(),
MLP_F7=double(),
MLP_F8=double(),
MLP_F9=double(),
MLP_F10=double(),
MLP_F11=double(),
MLP_F12=double(),
ACTUAL_1=double(),
ACTUAL_2=double(),
ACTUAL_3=double(),
ACTUAL_4=double(),
ACTUAL_5=double(),
ACTUAL_6=double(),
ACTUAL_7=double(),
ACTUAL_8=double(),
ACTUAL_9=double(),
ACTUAL_10=double(),
ACTUAL_11=double(),
ACTUAL_12=double(),
AR_F_Tally=double(),
AR_F_Conclusion=double(),
ARI_F_Tally=double(),
ARI_F_Conclusion=double(),
ARIS_F_Tally=double(),
ARIS_F_Conclusion=double(),
stringsAsFactors = FALSE)
# loop through sample combinations
for(i in 1:10) {
sample_combinations1 = sample_combinations[i,]
temp1 = inner_join(temp,sample_combinations1)
product = sample_combinations1$Product
customer = sample_combinations1$Customer_ID
product_type = sample_combinations1$Customer_ID
results[i,"Product_Type"] = product_type
results[i,"Product"] = as.character(sample_combinations1$Product)
results[i,"Customer"] = customer
par(mfrow=c(1,1))
plot.ts(temp1$STD_Cases,
main=c(paste("Standard Case Sales of ", product),
paste("for Customer",customer)),
xlab="Months",
ylab="Standard Cases")
par(mfrow = c(2,2))
invisible(acf(temp1$STD_Cases, main="ACF"))
invisible(parzen.wge(temp1$STD_Cases))
invisible(acf(temp1$STD_Cases[0:length(temp1$date)/2], main="ACF for 1st Half of Series"))
invisible(acf(temp1$STD_Cases[(1+length(temp1$date)/2):length(temp1$date)], main="ACF for 2nd Half of Series"))
sink("file")
ljung_10 = ljung.wge(temp1$STD_Cases,K=10)
sink()
cat("The Ljung-Box test with K=10 has a p-value of",ljung_10$pval,".")
results[i,"ljung_10"] = ljung_10$pval
sink("file")
ljung_24 = ljung.wge(temp1$STD_Cases,K=24)
sink()
cat("The Ljung-Box test with K=24 has a p-value of",ljung_24$pval,".")
results[i,"ljung_24"] = ljung_24$pval
if (ljung_10$pval < .05 & ljung_24$pval < .05){
print("Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise.")
results[i,"ljung_results"] = "not white noise"
} else if (ljung_10$pval > .05 & ljung_24$pval < .05){
print("Ljung-Box test results: At a significance level of 0.05, the test is inconclusive.")
results[i,"ljung_results"] = "inconclusive"
} else if (ljung_10$pval < .05 & ljung_24$pval > .05){
print("Ljung-Box test results: At a significance level of 0.05, the test is inconclusive.")
results[i,"ljung_results"] = "inconclusive"
} else {
print("Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise.")
results[i,"ljung_results"] = "white noise"
}
sink("file")
aic = invisible(aic5.wge(temp1$STD_Cases,type="bic"))
sink()
for (row in 1:nrow(aic)) {
if(aic[row,1] == 0 & aic[row,2] == 0){
print("One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise.")
results[i,"top_5_bic"] = "white noise"
}
}
# Tests for stationarity
# Augmented Dickey-Fuller
adf=tseries::adf.test(temp1$STD_Cases)
results[i,"ADF"] = adf$p.value
# Kwiatkowski-Phillips-Schmidt-Shin
kpss=tseries::kpss.test(temp1$STD_Cases)
results[i,"KPSS"] = kpss$p.value
if (adf$p.value < .05 & kpss$p.value > .05){
print("Both stationarity tests indicate this time series is stationary.")
results[i,"stationarity_results"] = "stationary"
} else if (adf$p.value >= .05 & kpss$p.value <= .05){
print("Both stationarity tests indicate this time series is NOT stationary.")
results[i,"stationarity_results"] = "not stationary"
} else {
print("Both tests for stationarity were inconclusive.")
results[i,"stationarity_results"] = "inconclusive"
}
j=12
#Equal Means Model
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model0_mean = mean(temp1$STD_Cases[k:(k+(trainingSize-1))])
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - model0_mean)^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - model0_mean)^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - model0_mean)^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - model0_mean)^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - model0_mean)^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - model0_mean)^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - model0_mean)^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - model0_mean)^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - model0_mean)^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - model0_mean)^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - model0_mean)^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - model0_mean)^2)
sink()
assign(paste("EqualMeans_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - model0_mean)^2)
assign(paste("EqualMeans_DF_",k,sep=""),trainingSize-1)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("EqualMeans_1_ASE")] = WindowedASE1
results[i,paste0("EqualMeans_2_ASE")] = WindowedASE2
results[i,paste0("EqualMeans_3_ASE")] = WindowedASE3
results[i,paste0("EqualMeans_4_ASE")] = WindowedASE4
results[i,paste0("EqualMeans_5_ASE")] = WindowedASE5
results[i,paste0("EqualMeans_6_ASE")] = WindowedASE6
results[i,paste0("EqualMeans_7_ASE")] = WindowedASE7
results[i,paste0("EqualMeans_8_ASE")] = WindowedASE8
results[i,paste0("EqualMeans_9_ASE")] = WindowedASE9
results[i,paste0("EqualMeans_10_ASE")] = WindowedASE10
results[i,paste0("EqualMeans_11_ASE")] = WindowedASE11
results[i,paste0("EqualMeans_12_ASE")] = WindowedASE12
results[i,paste0("EqualMeans_F1")] = model0_mean
results[i,paste0("EqualMeans_F2")] = model0_mean
results[i,paste0("EqualMeans_F3")] = model0_mean
results[i,paste0("EqualMeans_F4")] = model0_mean
results[i,paste0("EqualMeans_F5")] = model0_mean
results[i,paste0("EqualMeans_F6")] = model0_mean
results[i,paste0("EqualMeans_F7")] = model0_mean
results[i,paste0("EqualMeans_F8")] = model0_mean
results[i,paste0("EqualMeans_F9")] = model0_mean
results[i,paste0("EqualMeans_F10")] = model0_mean
results[i,paste0("EqualMeans_F11")] = model0_mean
results[i,paste0("EqualMeans_F12")] = model0_mean
#AR Model
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],q=0,type="aic"))
model1 = invisible(aic.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],q=0,type="aic"))
if (model1$p == 0){
newphi = 1
} else {
newphi = model1$p
}
model1_est = invisible(est.ar.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],p=newphi))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = 0, s = 0, d = 0,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("AR_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
assign(paste("AR_DF_",k,sep=""),trainingSize-(newphi+1))
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("AR_1_ASE")] = WindowedASE1
results[i,paste0("AR_2_ASE")] = WindowedASE2
results[i,paste0("AR_3_ASE")] = WindowedASE3
results[i,paste0("AR_4_ASE")] = WindowedASE4
results[i,paste0("AR_5_ASE")] = WindowedASE5
results[i,paste0("AR_6_ASE")] = WindowedASE6
results[i,paste0("AR_7_ASE")] = WindowedASE7
results[i,paste0("AR_8_ASE")] = WindowedASE8
results[i,paste0("AR_9_ASE")] = WindowedASE9
results[i,paste0("AR_10_ASE")] = WindowedASE10
results[i,paste0("AR_11_ASE")] = WindowedASE11
results[i,paste0("AR_12_ASE")] = WindowedASE12
results[i,paste0("AR_F1")] = forecasts$f[1]
results[i,paste0("AR_F2")] = forecasts$f[2]
results[i,paste0("AR_F3")] = forecasts$f[3]
results[i,paste0("AR_F4")] = forecasts$f[4]
results[i,paste0("AR_F5")] = forecasts$f[5]
results[i,paste0("AR_F6")] = forecasts$f[6]
results[i,paste0("AR_F7")] = forecasts$f[7]
results[i,paste0("AR_F8")] = forecasts$f[8]
results[i,paste0("AR_F9")] = forecasts$f[9]
results[i,paste0("AR_F10")] = forecasts$f[10]
results[i,paste0("AR_F11")] = forecasts$f[11]
results[i,paste0("AR_F12")] = forecasts$f[12]
#ARMA Model
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],type="aic"))
model1_est = invisible(est.arma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],p=model1$p,q=model1$q))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = model1_est$theta, s = 0, d = 0,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARMA_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARMA_1_ASE")] = WindowedASE1
results[i,paste0("ARMA_2_ASE")] = WindowedASE2
results[i,paste0("ARMA_3_ASE")] = WindowedASE3
results[i,paste0("ARMA_4_ASE")] = WindowedASE4
results[i,paste0("ARMA_5_ASE")] = WindowedASE5
results[i,paste0("ARMA_6_ASE")] = WindowedASE6
results[i,paste0("ARMA_7_ASE")] = WindowedASE7
results[i,paste0("ARMA_8_ASE")] = WindowedASE8
results[i,paste0("ARMA_9_ASE")] = WindowedASE9
results[i,paste0("ARMA_10_ASE")] = WindowedASE10
results[i,paste0("ARMA_11_ASE")] = WindowedASE11
results[i,paste0("ARMA_12_ASE")] = WindowedASE12
results[i,paste0("ARMA_F1")] = forecasts$f[1]
results[i,paste0("ARMA_F2")] = forecasts$f[2]
results[i,paste0("ARMA_F3")] = forecasts$f[3]
results[i,paste0("ARMA_F4")] = forecasts$f[4]
results[i,paste0("ARMA_F5")] = forecasts$f[5]
results[i,paste0("ARMA_F6")] = forecasts$f[6]
results[i,paste0("ARMA_F7")] = forecasts$f[7]
results[i,paste0("ARMA_F8")] = forecasts$f[8]
results[i,paste0("ARMA_F9")] = forecasts$f[9]
results[i,paste0("ARMA_F10")] = forecasts$f[10]
results[i,paste0("ARMA_F11")] = forecasts$f[11]
results[i,paste0("ARMA_F12")] = forecasts$f[12]
#ARIMA Model with q=0 and d=1
nulldev()
temp2 = artrans.wge(temp1$STD_Cases,1)
dev.off()
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp2[k:(k+(trainingSize-1-1))],q=0,type="aic"))
model1_est = invisible(est.ar.wge(temp2[k:(k+(trainingSize-1-1))],p=model1$p))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = 0, s = 0, d = 1,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARI_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
assign(paste("ARI_DF_",k,sep=""),trainingSize-(model1$p+1))
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARI_1_ASE")] = WindowedASE1
results[i,paste0("ARI_2_ASE")] = WindowedASE2
results[i,paste0("ARI_3_ASE")] = WindowedASE3
results[i,paste0("ARI_4_ASE")] = WindowedASE4
results[i,paste0("ARI_5_ASE")] = WindowedASE5
results[i,paste0("ARI_6_ASE")] = WindowedASE6
results[i,paste0("ARI_7_ASE")] = WindowedASE7
results[i,paste0("ARI_8_ASE")] = WindowedASE8
results[i,paste0("ARI_9_ASE")] = WindowedASE9
results[i,paste0("ARI_10_ASE")] = WindowedASE10
results[i,paste0("ARI_11_ASE")] = WindowedASE11
results[i,paste0("ARI_12_ASE")] = WindowedASE12
results[i,paste0("ARI_F1")] = forecasts$f[1]
results[i,paste0("ARI_F2")] = forecasts$f[2]
results[i,paste0("ARI_F3")] = forecasts$f[3]
results[i,paste0("ARI_F4")] = forecasts$f[4]
results[i,paste0("ARI_F5")] = forecasts$f[5]
results[i,paste0("ARI_F6")] = forecasts$f[6]
results[i,paste0("ARI_F7")] = forecasts$f[7]
results[i,paste0("ARI_F8")] = forecasts$f[8]
results[i,paste0("ARI_F9")] = forecasts$f[9]
results[i,paste0("ARI_F10")] = forecasts$f[10]
results[i,paste0("ARI_F11")] = forecasts$f[11]
results[i,paste0("ARI_F12")] = forecasts$f[12]
#ARIMA Model with d=1
nulldev()
temp2 = artrans.wge(temp1$STD_Cases,1)
dev.off()
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp2[k:(k+(trainingSize-1-1))],type="aic"))
model1_est = invisible(est.arma.wge(temp2[k:(k+(trainingSize-1-1))],p=model1$p,q=model1$q))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = model1_est$theta, s = 0, d = 1,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARIMA_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARIMA_1_ASE")] = WindowedASE1
results[i,paste0("ARIMA_2_ASE")] = WindowedASE2
results[i,paste0("ARIMA_3_ASE")] = WindowedASE3
results[i,paste0("ARIMA_4_ASE")] = WindowedASE4
results[i,paste0("ARIMA_5_ASE")] = WindowedASE5
results[i,paste0("ARIMA_6_ASE")] = WindowedASE6
results[i,paste0("ARIMA_7_ASE")] = WindowedASE7
results[i,paste0("ARIMA_8_ASE")] = WindowedASE8
results[i,paste0("ARIMA_9_ASE")] = WindowedASE9
results[i,paste0("ARIMA_10_ASE")] = WindowedASE10
results[i,paste0("ARIMA_11_ASE")] = WindowedASE11
results[i,paste0("ARIMA_12_ASE")] = WindowedASE12
results[i,paste0("ARIMA_F1")] = forecasts$f[1]
results[i,paste0("ARIMA_F2")] = forecasts$f[2]
results[i,paste0("ARIMA_F3")] = forecasts$f[3]
results[i,paste0("ARIMA_F4")] = forecasts$f[4]
results[i,paste0("ARIMA_F5")] = forecasts$f[5]
results[i,paste0("ARIMA_F6")] = forecasts$f[6]
results[i,paste0("ARIMA_F7")] = forecasts$f[7]
results[i,paste0("ARIMA_F8")] = forecasts$f[8]
results[i,paste0("ARIMA_F9")] = forecasts$f[9]
results[i,paste0("ARIMA_F10")] = forecasts$f[10]
results[i,paste0("ARIMA_F11")] = forecasts$f[11]
results[i,paste0("ARIMA_F12")] = forecasts$f[12]
#ARIMA Model with q=0 and S=12
nulldev()
temp2 = artrans.wge(temp1$STD_Cases,phi.tr=c(rep(0,11),1))
dev.off()
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp2[k:(k+(trainingSize-1-12))],q=0, type="aic"))
if (model1$p == 0){
newphi = 1
} else {
newphi = model1$p
}
model1_est = invisible(est.ar.wge(temp2[k:(k+(trainingSize-1-12))],p=newphi))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = 0, s = 12, d = 0,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARIS_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
assign(paste("ARIS_DF_",k,sep=""),trainingSize-(newphi+1))
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARI_S12_1_ASE")] = WindowedASE1
results[i,paste0("ARI_S12_2_ASE")] = WindowedASE2
results[i,paste0("ARI_S12_3_ASE")] = WindowedASE3
results[i,paste0("ARI_S12_4_ASE")] = WindowedASE4
results[i,paste0("ARI_S12_5_ASE")] = WindowedASE5
results[i,paste0("ARI_S12_6_ASE")] = WindowedASE6
results[i,paste0("ARI_S12_7_ASE")] = WindowedASE7
results[i,paste0("ARI_S12_8_ASE")] = WindowedASE8
results[i,paste0("ARI_S12_9_ASE")] = WindowedASE9
results[i,paste0("ARI_S12_10_ASE")] = WindowedASE10
results[i,paste0("ARI_S12_11_ASE")] = WindowedASE11
results[i,paste0("ARI_S12_12_ASE")] = WindowedASE12
results[i,paste0("ARI_S12_F1")] = forecasts$f[1]
results[i,paste0("ARI_S12_F2")] = forecasts$f[2]
results[i,paste0("ARI_S12_F3")] = forecasts$f[3]
results[i,paste0("ARI_S12_F4")] = forecasts$f[4]
results[i,paste0("ARI_S12_F5")] = forecasts$f[5]
results[i,paste0("ARI_S12_F6")] = forecasts$f[6]
results[i,paste0("ARI_S12_F7")] = forecasts$f[7]
results[i,paste0("ARI_S12_F8")] = forecasts$f[8]
results[i,paste0("ARI_S12_F9")] = forecasts$f[9]
results[i,paste0("ARI_S12_F10")] = forecasts$f[10]
results[i,paste0("ARI_S12_F11")] = forecasts$f[11]
results[i,paste0("ARI_S12_F12")] = forecasts$f[12]
#ARIMA Model with S=12
nulldev()
temp2 = artrans.wge(temp1$STD_Cases,phi.tr=c(rep(0,11),1))
dev.off()
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp2[k:(k+(trainingSize-1-12))],type="aic"))
model1_est = invisible(est.arma.wge(temp2[k:(k+(trainingSize-1-12))],p=model1$p,q=model1$q))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = model1_est$theta, s = 12, d = 0,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARIMAS_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARIMA_S12_1_ASE")] = WindowedASE1
results[i,paste0("ARIMA_S12_2_ASE")] = WindowedASE2
results[i,paste0("ARIMA_S12_3_ASE")] = WindowedASE3
results[i,paste0("ARIMA_S12_4_ASE")] = WindowedASE4
results[i,paste0("ARIMA_S12_5_ASE")] = WindowedASE5
results[i,paste0("ARIMA_S12_6_ASE")] = WindowedASE6
results[i,paste0("ARIMA_S12_7_ASE")] = WindowedASE7
results[i,paste0("ARIMA_S12_8_ASE")] = WindowedASE8
results[i,paste0("ARIMA_S12_9_ASE")] = WindowedASE9
results[i,paste0("ARIMA_S12_10_ASE")] = WindowedASE10
results[i,paste0("ARIMA_S12_11_ASE")] = WindowedASE11
results[i,paste0("ARIMA_S12_12_ASE")] = WindowedASE12
results[i,paste0("ARIMA_S12_F1")] = forecasts$f[1]
results[i,paste0("ARIMA_S12_F2")] = forecasts$f[2]
results[i,paste0("ARIMA_S12_F3")] = forecasts$f[3]
results[i,paste0("ARIMA_S12_F4")] = forecasts$f[4]
results[i,paste0("ARIMA_S12_F5")] = forecasts$f[5]
results[i,paste0("ARIMA_S12_F6")] = forecasts$f[6]
results[i,paste0("ARIMA_S12_F7")] = forecasts$f[7]
results[i,paste0("ARIMA_S12_F8")] = forecasts$f[8]
results[i,paste0("ARIMA_S12_F9")] = forecasts$f[9]
results[i,paste0("ARIMA_S12_F10")] = forecasts$f[10]
results[i,paste0("ARIMA_S12_F11")] = forecasts$f[11]
results[i,paste0("ARIMA_S12_F12")] = forecasts$f[12]
#Random Forest
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
forecasts <- rf_ts(j, temp1[k:(k+(trainingSize-1)),], FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$forecast[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$forecast[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$forecast[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$forecast[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$forecast[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$forecast[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$forecast[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$forecast[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$forecast[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$forecast[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$forecast[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$forecast[1:12])^2)
sink()
assign(paste("RF_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$forecast)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("RF_1_ASE")] = WindowedASE1
results[i,paste0("RF_2_ASE")] = WindowedASE2
results[i,paste0("RF_3_ASE")] = WindowedASE3
results[i,paste0("RF_4_ASE")] = WindowedASE4
results[i,paste0("RF_5_ASE")] = WindowedASE5
results[i,paste0("RF_6_ASE")] = WindowedASE6
results[i,paste0("RF_7_ASE")] = WindowedASE7
results[i,paste0("RF_8_ASE")] = WindowedASE8
results[i,paste0("RF_9_ASE")] = WindowedASE9
results[i,paste0("RF_10_ASE")] = WindowedASE10
results[i,paste0("RF_11_ASE")] = WindowedASE11
results[i,paste0("RF_12_ASE")] = WindowedASE12
results[i,paste0("RF_F1")] = forecasts$forecast[1]
results[i,paste0("RF_F2")] = forecasts$forecast[2]
results[i,paste0("RF_F3")] = forecasts$forecast[3]
results[i,paste0("RF_F4")] = forecasts$forecast[4]
results[i,paste0("RF_F5")] = forecasts$forecast[5]
results[i,paste0("RF_F6")] = forecasts$forecast[6]
results[i,paste0("RF_F7")] = forecasts$forecast[7]
results[i,paste0("RF_F8")] = forecasts$forecast[8]
results[i,paste0("RF_F9")] = forecasts$forecast[9]
results[i,paste0("RF_F10")] = forecasts$forecast[10]
results[i,paste0("RF_F11")] = forecasts$forecast[11]
results[i,paste0("RF_F12")] = forecasts$forecast[12]
# MLP
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
forecasts <- nnc(temp1$STD_Cases[k:(k+(trainingSize-1))], j, 10, c(5, 10, 15, 5), FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$forecast[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$forecast[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$forecast[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$forecast[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$forecast[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$forecast[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$forecast[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$forecast[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$forecast[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$forecast[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$forecast[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$forecast[1:12])^2)
sink()
assign(paste("MLP_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$forecast)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("MLP_1_ASE")] = WindowedASE1
results[i,paste0("MLP_2_ASE")] = WindowedASE2
results[i,paste0("MLP_3_ASE")] = WindowedASE3
results[i,paste0("MLP_4_ASE")] = WindowedASE4
results[i,paste0("MLP_5_ASE")] = WindowedASE5
results[i,paste0("MLP_6_ASE")] = WindowedASE6
results[i,paste0("MLP_7_ASE")] = WindowedASE7
results[i,paste0("MLP_8_ASE")] = WindowedASE8
results[i,paste0("MLP_9_ASE")] = WindowedASE9
results[i,paste0("MLP_10_ASE")] = WindowedASE10
results[i,paste0("MLP_11_ASE")] = WindowedASE11
results[i,paste0("MLP_12_ASE")] = WindowedASE12
results[i,paste0("MLP_F1")] = forecasts$forecast[1]
results[i,paste0("MLP_F2")] = forecasts$forecast[2]
results[i,paste0("MLP_F3")] = forecasts$forecast[3]
results[i,paste0("MLP_F4")] = forecasts$forecast[4]
results[i,paste0("MLP_F5")] = forecasts$forecast[5]
results[i,paste0("MLP_F6")] = forecasts$forecast[6]
results[i,paste0("MLP_F7")] = forecasts$forecast[7]
results[i,paste0("MLP_F8")] = forecasts$forecast[8]
results[i,paste0("MLP_F9")] = forecasts$forecast[9]
results[i,paste0("MLP_F10")] = forecasts$forecast[10]
results[i,paste0("MLP_F11")] = forecasts$forecast[11]
results[i,paste0("MLP_F12")] = forecasts$forecast[12]
results[i,paste0("ACTUAL_1")] = temp1$STD_Cases[73]
results[i,paste0("ACTUAL_2")] = temp1$STD_Cases[74]
results[i,paste0("ACTUAL_3")] = temp1$STD_Cases[75]
results[i,paste0("ACTUAL_4")] = temp1$STD_Cases[76]
results[i,paste0("ACTUAL_5")] = temp1$STD_Cases[77]
results[i,paste0("ACTUAL_6")] = temp1$STD_Cases[78]
results[i,paste0("ACTUAL_7")] = temp1$STD_Cases[79]
results[i,paste0("ACTUAL_8")] = temp1$STD_Cases[80]
results[i,paste0("ACTUAL_9")] = temp1$STD_Cases[81]
results[i,paste0("ACTUAL_10")] = temp1$STD_Cases[82]
results[i,paste0("ACTUAL_11")] = temp1$STD_Cases[83]
results[i,paste0("ACTUAL_12")] = temp1$STD_Cases[84]
#graph ASEs for each Model
EqualMeans_Results <- rbind(EqualMeans_Results_1,EqualMeans_Results_2,EqualMeans_Results_3,EqualMeans_Results_4,EqualMeans_Results_5,EqualMeans_Results_6,
EqualMeans_Results_7,EqualMeans_Results_8,EqualMeans_Results_9,EqualMeans_Results_10,EqualMeans_Results_11,EqualMeans_Results_12,
EqualMeans_Results_13)
AR_Results <- rbind(AR_Results_1,AR_Results_2,AR_Results_3,AR_Results_4,AR_Results_5,AR_Results_6,AR_Results_7,AR_Results_8,AR_Results_9,AR_Results_10,
AR_Results_11,AR_Results_12,AR_Results_13)
ARMA_Results <- rbind(ARMA_Results_1,ARMA_Results_2,ARMA_Results_3,ARMA_Results_4,ARMA_Results_5,ARMA_Results_6,ARMA_Results_7,ARMA_Results_8,
ARMA_Results_9,ARMA_Results_10,ARMA_Results_11,ARMA_Results_12,ARMA_Results_13)
ARI_Results <- rbind(ARI_Results_1,ARI_Results_2,ARI_Results_3,ARI_Results_4,ARI_Results_5,ARI_Results_6,ARI_Results_7,ARI_Results_8,
ARI_Results_9,ARI_Results_10,ARI_Results_11,ARI_Results_12,ARI_Results_13)
ARIMA_Results <- rbind(ARIMA_Results_1,ARIMA_Results_2,ARIMA_Results_3,ARIMA_Results_4,ARIMA_Results_5,ARIMA_Results_6,ARIMA_Results_7,ARIMA_Results_8,
ARIMA_Results_9,ARIMA_Results_10,ARIMA_Results_11,ARIMA_Results_12,ARIMA_Results_13)
ARIS_Results <- rbind(ARIS_Results_1,ARIS_Results_2,ARIS_Results_3,ARIS_Results_4,ARIS_Results_5,ARIS_Results_6,ARIS_Results_7,ARIS_Results_8,
ARIS_Results_9,ARIS_Results_10,ARIS_Results_11,ARIS_Results_12,ARIS_Results_13)
ARIMAS_Results <- rbind(ARIMAS_Results_1,ARIMAS_Results_2,ARIMAS_Results_3,ARIMAS_Results_4,ARIMAS_Results_5,ARIMAS_Results_6,ARIMAS_Results_7,ARIMAS_Results_8,
ARIMAS_Results_9,ARIMAS_Results_10,ARIMAS_Results_11,ARIMAS_Results_12,ARIMAS_Results_13)
RF_Results <- rbind(RF_Results_1,RF_Results_2,RF_Results_3,RF_Results_4,RF_Results_5,RF_Results_6,RF_Results_7,RF_Results_8,
RF_Results_9,RF_Results_10,RF_Results_11,RF_Results_12,RF_Results_13)
MLP_Results <- rbind(MLP_Results_1,MLP_Results_2,MLP_Results_3,MLP_Results_4,MLP_Results_5,MLP_Results_6,MLP_Results_7,MLP_Results_8,
MLP_Results_9,MLP_Results_10,MLP_Results_11,MLP_Results_12,MLP_Results_13)
EqualMeans_Means <- colMeans(EqualMeans_Results)
AR_Means <- colMeans(AR_Results)
ARMA_Means <- colMeans(ARMA_Results)
ARI_Means <- colMeans(ARI_Results)
ARIMA_Means <- colMeans(ARIMA_Results)
ARIS_Means <- colMeans(ARIS_Results)
ARIMAS_Means <- colMeans(ARIMAS_Results)
RF_Means <- colMeans(RF_Results)
MLP_Means <- colMeans(MLP_Results)
Combined_Means <- data.frame(EqualMeans_Means,AR_Means, ARMA_Means, ARI_Means, ARIMA_Means, ARIS_Means, ARIMAS_Means,RF_Means,MLP_Means)
Combined_Means$horizon <- as.numeric(row.names(Combined_Means))
# more colors #73EBAE
g <- ggplot(data=Combined_Means, aes(horizon)) +
geom_line(aes(y=EqualMeans_Means, color="Equal Means"),size=1.5) +
geom_line(aes(y=AR_Means, color="AR"),size=1.5) +
geom_line(aes(y=ARMA_Means, color="ARMA"),size=1.5) +
geom_line(aes(y=ARI_Means, color="AR with d=1"),size=1.5) +
geom_line(aes(y=ARIMA_Means, color="ARIMA with d=1"),size=1.5) +
geom_line(aes(y=ARIS_Means, color="AR with s=12"),size=1.5) +
geom_line(aes(y=ARIMAS_Means, color="ARIMA with d=0, s=12"),size=1.5) +
geom_line(aes(y=RF_Means, color="Random Forest"),size=1.5) +
geom_line(aes(y=MLP_Means, color="MLP"),size=1.5) +
scale_color_manual(values = c(
'Equal Means' = '#004159',
'AR' = '#65A8C4',
'ARMA' = '#8C65D3',
'AR with d=1' = '#9A93EC',
'ARIMA with d=1' = '#0052A5',
'AR with s=12' = '#413BF7',
'ARIMA with d=0, s=12' = '#00ADCE',
'Random Forest' = '#59DBF1',
'MLP' = '#00C590'
)) +
labs(color='Models') +
scale_x_continuous(breaks=seq(0,13,1)) +
ggtitle(paste("Model ASEs for ", product,"and Customer",customer)) +
xlab("Month Ahead Forecast") +
ylab("ASE") +
theme(panel.background = element_blank(), axis.line = element_line(colour = "black"), legend.title = element_blank())
print(g)
# f-statistic calculations
EqualMeans_DF <- rbind(EqualMeans_DF_1,EqualMeans_DF_2,EqualMeans_DF_3,EqualMeans_DF_4,EqualMeans_DF_5,EqualMeans_DF_6,EqualMeans_DF_7,
EqualMeans_DF_8,EqualMeans_DF_9,EqualMeans_DF_10,EqualMeans_DF_11,EqualMeans_DF_12,EqualMeans_DF_13)
AR_DF <- rbind(AR_DF_1,AR_DF_2,AR_DF_3,AR_DF_4,AR_DF_5,AR_DF_6,AR_DF_7,AR_DF_8,AR_DF_9,AR_DF_10,AR_DF_11,AR_DF_12,AR_DF_13)
ARI_DF <- rbind(ARI_DF_1,ARI_DF_2,ARI_DF_3,ARI_DF_4,ARI_DF_5,ARI_DF_6,ARI_DF_7,ARI_DF_8,ARI_DF_9,ARI_DF_10,ARI_DF_11,ARI_DF_12,ARI_DF_13)
ARIS_DF <- rbind(ARIS_DF_1,ARIS_DF_2,ARIS_DF_3,ARIS_DF_4,ARIS_DF_5,ARIS_DF_6,ARIS_DF_7,ARIS_DF_8,ARIS_DF_9,ARIS_DF_10,ARIS_DF_11,ARIS_DF_12,ARIS_DF_13)
EqualMeans_Results <- rbind(sum(EqualMeans_Results_1),sum(EqualMeans_Results_2),sum(EqualMeans_Results_3),sum(EqualMeans_Results_4),sum(EqualMeans_Results_5),
sum(EqualMeans_Results_6),sum(EqualMeans_Results_7),sum(EqualMeans_Results_8),sum(EqualMeans_Results_9),sum(EqualMeans_Results_10),
sum(EqualMeans_Results_11),sum(EqualMeans_Results_12),sum(EqualMeans_Results_13))
AR_Results <- rbind(sum(AR_Results_1),sum(AR_Results_2),sum(AR_Results_3),sum(AR_Results_4),sum(AR_Results_5),sum(AR_Results_6),sum(AR_Results_7),
sum(AR_Results_8),sum(AR_Results_9),sum(AR_Results_10),sum(AR_Results_11),sum(AR_Results_12),sum(AR_Results_13))
ARI_Results <- rbind(sum(ARI_Results_1),sum(ARI_Results_2),sum(ARI_Results_3),sum(ARI_Results_4),sum(ARI_Results_5),sum(ARI_Results_6),sum(ARI_Results_7),
sum(ARI_Results_8),sum(ARI_Results_9),sum(ARI_Results_10),sum(ARI_Results_11),sum(ARI_Results_12),sum(ARI_Results_13))
ARIS_Results <- rbind(sum(ARIS_Results_1),sum(ARIS_Results_2),sum(ARIS_Results_3),sum(ARIS_Results_4),sum(ARIS_Results_5),sum(ARIS_Results_6),sum(ARIS_Results_7),
sum(ARIS_Results_8),sum(ARIS_Results_9),sum(ARIS_Results_10),sum(ARIS_Results_11),sum(ARIS_Results_12),sum(ARIS_Results_13))
df_model = EqualMeans_DF - AR_DF
ss_model = EqualMeans_Results - AR_Results
ms_model = ss_model/df_model
ms_ar = AR_Results/AR_DF
F = ms_model/ms_ar
AR_p_value = pf(F,df_model,AR_DF,lower.tail=FALSE)
AR_p_tally = sum(AR_p_value[,1]<.05)
results[i,"AR_F_Tally"] = AR_p_tally
if (AR_p_tally >= 9){
results[i,"AR_F_Conclusion"] = "Different"
} else if (AR_p_tally <= 4){
results[i,"AR_F_Conclusion"] = "Same"
} else {
results[i,"AR_F_Conclusion"] = "Inconclusive"
}
df_model = EqualMeans_DF - ARI_DF
ss_model = EqualMeans_Results - ARI_Results
ms_model = ss_model/df_model
ms_ari = ARI_Results/ARI_DF
F = ms_model/ms_ari
ARI_p_value = pf(F,df_model,ARI_DF,lower.tail=FALSE)
ARI_p_tally = sum(ARI_p_value[,1]<.05)
results[i,"ARI_F_Tally"] = ARI_p_tally
if (ARI_p_tally >= 9){
results[i,"ARI_F_Conclusion"] = "Different"
} else if (ARI_p_tally <= 4){
results[i,"ARI_F_Conclusion"] = "Same"
} else {
results[i,"ARI_F_Conclusion"] = "Inconclusive"
}
df_model = EqualMeans_DF - ARIS_DF
ss_model = EqualMeans_Results - ARIS_Results
ms_model = ss_model/df_model
ms_aris = ARIS_Results/ARIS_DF
F = ms_model/ms_aris
ARIS_p_value = pf(F,df_model,ARIS_DF,lower.tail=FALSE)
ARIS_p_tally = sum(ARIS_p_value[,1]<.05)
results[i,"ARIS_F_Tally"] = ARIS_p_tally
if (ARIS_p_tally >= 9){
results[i,"ARIS_F_Conclusion"] = "Different"
} else if (ARIS_p_tally <= 4){
results[i,"ARIS_F_Conclusion"] = "Same"
} else {
results[i,"ARIS_F_Conclusion"] = "Inconclusive"
}
}

The Ljung-Box test with K=10 has a p-value of 4.786171e-12 .The Ljung-Box test with K=24 has a p-value of 1.665335e-15 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.08807377 .The Ljung-Box test with K=24 has a p-value of 0.3665865 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0.3801776 .The Ljung-Box test with K=24 has a p-value of 0.1126704 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.0009489966 .The Ljung-Box test with K=24 has a p-value of 0.0002502709 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.0186233 .The Ljung-Box test with K=24 has a p-value of 0.005561031 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 6.426664e-08 .The Ljung-Box test with K=24 has a p-value of 6.568032e-10 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 2.934075e-10 .The Ljung-Box test with K=24 has a p-value of 3.874856e-11 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.6360152 .The Ljung-Box test with K=24 has a p-value of 0.844331 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is stationary."



The Ljung-Box test with K=10 has a p-value of 0.06656046 .The Ljung-Box test with K=24 has a p-value of 0.0002436308 .[1] "Ljung-Box test results: At a significance level of 0.05, the test is inconclusive."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.02684747 .The Ljung-Box test with K=24 has a p-value of 0.00508631 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."


results$winning_1 <- colnames(results[c("EqualMeans_1_ASE","AR_1_ASE","ARMA_1_ASE","ARI_1_ASE","ARIMA_1_ASE","ARI_S12_1_ASE","ARIMA_S12_1_ASE","RF_1_ASE","MLP_1_ASE")])[apply(results[c("EqualMeans_1_ASE","AR_1_ASE","ARMA_1_ASE","ARI_1_ASE","ARIMA_1_ASE","ARI_S12_1_ASE","ARIMA_S12_1_ASE","RF_1_ASE","MLP_1_ASE")],1,which.min)]
results$winning_2 <- colnames(results[c("EqualMeans_2_ASE","AR_2_ASE","ARMA_2_ASE","ARI_2_ASE","ARIMA_2_ASE","ARI_S12_2_ASE","ARIMA_S12_2_ASE","RF_2_ASE","MLP_2_ASE")])[apply(results[c("EqualMeans_2_ASE","AR_2_ASE","ARMA_2_ASE","ARI_2_ASE","ARIMA_2_ASE","ARI_S12_2_ASE","ARIMA_S12_2_ASE","RF_2_ASE","MLP_2_ASE")],1,which.min)]
results$winning_3 <- colnames(results[c("EqualMeans_3_ASE","AR_3_ASE","ARMA_3_ASE","ARI_3_ASE","ARIMA_3_ASE","ARI_S12_3_ASE","ARIMA_S12_3_ASE","RF_3_ASE","MLP_3_ASE")])[apply(results[c("EqualMeans_3_ASE","AR_3_ASE","ARMA_3_ASE","ARI_3_ASE","ARIMA_3_ASE","ARI_S12_3_ASE","ARIMA_S12_3_ASE","RF_3_ASE","MLP_3_ASE")],1,which.min)]
results$winning_4 <- colnames(results[c("EqualMeans_4_ASE","AR_4_ASE","ARMA_4_ASE","ARI_4_ASE","ARIMA_4_ASE","ARI_S12_4_ASE","ARIMA_S12_4_ASE","RF_4_ASE","MLP_4_ASE")])[apply(results[c("EqualMeans_4_ASE","AR_4_ASE","ARMA_4_ASE","ARI_4_ASE","ARIMA_4_ASE","ARI_S12_4_ASE","ARIMA_S12_4_ASE","RF_4_ASE","MLP_4_ASE")],1,which.min)]
results$winning_5 <- colnames(results[c("EqualMeans_5_ASE","AR_5_ASE","ARMA_5_ASE","ARI_5_ASE","ARIMA_5_ASE","ARI_S12_5_ASE","ARIMA_S12_5_ASE","RF_5_ASE","MLP_5_ASE")])[apply(results[c("EqualMeans_5_ASE","AR_5_ASE","ARMA_5_ASE","ARI_5_ASE","ARIMA_5_ASE","ARI_S12_5_ASE","ARIMA_S12_5_ASE","RF_5_ASE","MLP_5_ASE")],1,which.min)]
results$winning_6 <- colnames(results[c("EqualMeans_6_ASE","AR_6_ASE","ARMA_6_ASE","ARI_6_ASE","ARIMA_6_ASE","ARI_S12_6_ASE","ARIMA_S12_6_ASE","RF_6_ASE","MLP_6_ASE")])[apply(results[c("EqualMeans_6_ASE","AR_6_ASE","ARMA_6_ASE","ARI_6_ASE","ARIMA_6_ASE","ARI_S12_6_ASE","ARIMA_S12_6_ASE","RF_6_ASE","MLP_6_ASE")],1,which.min)]
results$winning_7 <- colnames(results[c("EqualMeans_7_ASE","AR_7_ASE","ARMA_7_ASE","ARI_7_ASE","ARIMA_7_ASE","ARI_S12_7_ASE","ARIMA_S12_7_ASE","RF_7_ASE","MLP_7_ASE")])[apply(results[c("EqualMeans_7_ASE","AR_7_ASE","ARMA_7_ASE","ARI_7_ASE","ARIMA_7_ASE","ARI_S12_7_ASE","ARIMA_S12_7_ASE","RF_7_ASE","MLP_7_ASE")],1,which.min)]
results$winning_8 <- colnames(results[c("EqualMeans_8_ASE","AR_8_ASE","ARMA_8_ASE","ARI_8_ASE","ARIMA_8_ASE","ARI_S12_8_ASE","ARIMA_S12_8_ASE","RF_8_ASE","MLP_8_ASE")])[apply(results[c("EqualMeans_8_ASE","AR_8_ASE","ARMA_8_ASE","ARI_8_ASE","ARIMA_8_ASE","ARI_S12_8_ASE","ARIMA_S12_8_ASE","RF_8_ASE","MLP_8_ASE")],1,which.min)]
results$winning_9 <- colnames(results[c("EqualMeans_9_ASE","AR_9_ASE","ARMA_9_ASE","ARI_9_ASE","ARIMA_9_ASE","ARI_S12_9_ASE","ARIMA_S12_9_ASE","RF_9_ASE","MLP_9_ASE")])[apply(results[c("EqualMeans_9_ASE","AR_9_ASE","ARMA_9_ASE","ARI_9_ASE","ARIMA_9_ASE","ARI_S12_9_ASE","ARIMA_S12_9_ASE","RF_9_ASE","MLP_9_ASE")],1,which.min)]
results$winning_10 <- colnames(results[c("EqualMeans_10_ASE","AR_10_ASE","ARMA_10_ASE","ARI_10_ASE","ARIMA_10_ASE","ARI_S12_10_ASE","ARIMA_S12_10_ASE","RF_10_ASE","MLP_10_ASE")])[apply(results[c("EqualMeans_10_ASE","AR_10_ASE","ARMA_10_ASE","ARI_10_ASE","ARIMA_10_ASE","ARI_S12_10_ASE","ARIMA_S12_10_ASE","RF_10_ASE","MLP_10_ASE")],1,which.min)]
results$winning_11 <- colnames(results[c("EqualMeans_11_ASE","AR_11_ASE","ARMA_11_ASE","ARI_11_ASE","ARIMA_11_ASE","ARI_S12_11_ASE","ARIMA_S12_11_ASE","RF_11_ASE","MLP_11_ASE")])[apply(results[c("EqualMeans_11_ASE","AR_11_ASE","ARMA_11_ASE","ARI_11_ASE","ARIMA_11_ASE","ARI_S12_11_ASE","ARIMA_S12_11_ASE","RF_11_ASE","MLP_11_ASE")],1,which.min)]
results$winning_12 <- colnames(results[c("EqualMeans_12_ASE","AR_12_ASE","ARMA_12_ASE","ARI_12_ASE","ARIMA_12_ASE","ARI_S12_12_ASE","ARIMA_S12_12_ASE","RF_12_ASE","MLP_12_ASE")])[apply(results[c("EqualMeans_12_ASE","AR_12_ASE","ARMA_12_ASE","ARI_12_ASE","ARIMA_12_ASE","ARI_S12_12_ASE","ARIMA_S12_12_ASE","RF_12_ASE","MLP_12_ASE")],1,which.min)]
formattable(results, align = c("l", rep("r", NCOL(table_a) - 1)))
|
Product_Type
|
Product
|
Customer
|
ljung_10
|
ljung_24
|
ljung_results
|
top_5_bic
|
ADF
|
KPSS
|
stationarity_results
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
EqualMeans_F1
|
EqualMeans_F2
|
EqualMeans_F3
|
EqualMeans_F4
|
EqualMeans_F5
|
EqualMeans_F6
|
EqualMeans_F7
|
EqualMeans_F8
|
EqualMeans_F9
|
EqualMeans_F10
|
EqualMeans_F11
|
EqualMeans_F12
|
AR_1_ASE
|
AR_2_ASE
|
AR_3_ASE
|
AR_4_ASE
|
AR_5_ASE
|
AR_6_ASE
|
AR_7_ASE
|
AR_8_ASE
|
AR_9_ASE
|
AR_10_ASE
|
AR_11_ASE
|
AR_12_ASE
|
AR_F1
|
AR_F2
|
AR_F3
|
AR_F4
|
AR_F5
|
AR_F6
|
AR_F7
|
AR_F8
|
AR_F9
|
AR_F10
|
AR_F11
|
AR_F12
|
ARMA_1_ASE
|
ARMA_2_ASE
|
ARMA_3_ASE
|
ARMA_4_ASE
|
ARMA_5_ASE
|
ARMA_6_ASE
|
ARMA_7_ASE
|
ARMA_8_ASE
|
ARMA_9_ASE
|
ARMA_10_ASE
|
ARMA_11_ASE
|
ARMA_12_ASE
|
ARMA_F1
|
ARMA_F2
|
ARMA_F3
|
ARMA_F4
|
ARMA_F5
|
ARMA_F6
|
ARMA_F7
|
ARMA_F8
|
ARMA_F9
|
ARMA_F10
|
ARMA_F11
|
ARMA_F12
|
ARI_1_ASE
|
ARI_2_ASE
|
ARI_3_ASE
|
ARI_4_ASE
|
ARI_5_ASE
|
ARI_6_ASE
|
ARI_7_ASE
|
ARI_8_ASE
|
ARI_9_ASE
|
ARI_10_ASE
|
ARI_11_ASE
|
ARI_12_ASE
|
ARI_F1
|
ARI_F2
|
ARI_F3
|
ARI_F4
|
ARI_F5
|
ARI_F6
|
ARI_F7
|
ARI_F8
|
ARI_F9
|
ARI_F10
|
ARI_F11
|
ARI_F12
|
ARIMA_1_ASE
|
ARIMA_2_ASE
|
ARIMA_3_ASE
|
ARIMA_4_ASE
|
ARIMA_5_ASE
|
ARIMA_6_ASE
|
ARIMA_7_ASE
|
ARIMA_8_ASE
|
ARIMA_9_ASE
|
ARIMA_10_ASE
|
ARIMA_11_ASE
|
ARIMA_12_ASE
|
ARIMA_F1
|
ARIMA_F2
|
ARIMA_F3
|
ARIMA_F4
|
ARIMA_F5
|
ARIMA_F6
|
ARIMA_F7
|
ARIMA_F8
|
ARIMA_F9
|
ARIMA_F10
|
ARIMA_F11
|
ARIMA_F12
|
ARI_S12_1_ASE
|
ARI_S12_2_ASE
|
ARI_S12_3_ASE
|
ARI_S12_4_ASE
|
ARI_S12_5_ASE
|
ARI_S12_6_ASE
|
ARI_S12_7_ASE
|
ARI_S12_8_ASE
|
ARI_S12_9_ASE
|
ARI_S12_10_ASE
|
ARI_S12_11_ASE
|
ARI_S12_12_ASE
|
ARI_S12_F1
|
ARI_S12_F2
|
ARI_S12_F3
|
ARI_S12_F4
|
ARI_S12_F5
|
ARI_S12_F6
|
ARI_S12_F7
|
ARI_S12_F8
|
ARI_S12_F9
|
ARI_S12_F10
|
ARI_S12_F11
|
ARI_S12_F12
|
ARIMA_S12_1_ASE
|
ARIMA_S12_2_ASE
|
ARIMA_S12_3_ASE
|
ARIMA_S12_4_ASE
|
ARIMA_S12_5_ASE
|
ARIMA_S12_6_ASE
|
ARIMA_S12_7_ASE
|
ARIMA_S12_8_ASE
|
ARIMA_S12_9_ASE
|
ARIMA_S12_10_ASE
|
ARIMA_S12_11_ASE
|
ARIMA_S12_12_ASE
|
ARIMA_S12_F1
|
ARIMA_S12_F2
|
ARIMA_S12_F3
|
ARIMA_S12_F4
|
ARIMA_S12_F5
|
ARIMA_S12_F6
|
ARIMA_S12_F7
|
ARIMA_S12_F8
|
ARIMA_S12_F9
|
ARIMA_S12_F10
|
ARIMA_S12_F11
|
ARIMA_S12_F12
|
RF_1_ASE
|
RF_2_ASE
|
RF_3_ASE
|
RF_4_ASE
|
RF_5_ASE
|
RF_6_ASE
|
RF_7_ASE
|
RF_8_ASE
|
RF_9_ASE
|
RF_10_ASE
|
RF_11_ASE
|
RF_12_ASE
|
RF_F1
|
RF_F2
|
RF_F3
|
RF_F4
|
RF_F5
|
RF_F6
|
RF_F7
|
RF_F8
|
RF_F9
|
RF_F10
|
RF_F11
|
RF_F12
|
MLP_1_ASE
|
MLP_2_ASE
|
MLP_3_ASE
|
MLP_4_ASE
|
MLP_5_ASE
|
MLP_6_ASE
|
MLP_7_ASE
|
MLP_8_ASE
|
MLP_9_ASE
|
MLP_10_ASE
|
MLP_11_ASE
|
MLP_12_ASE
|
MLP_F1
|
MLP_F2
|
MLP_F3
|
MLP_F4
|
MLP_F5
|
MLP_F6
|
MLP_F7
|
MLP_F8
|
MLP_F9
|
MLP_F10
|
MLP_F11
|
MLP_F12
|
ACTUAL_1
|
ACTUAL_2
|
ACTUAL_3
|
ACTUAL_4
|
ACTUAL_5
|
ACTUAL_6
|
ACTUAL_7
|
ACTUAL_8
|
ACTUAL_9
|
ACTUAL_10
|
ACTUAL_11
|
ACTUAL_12
|
AR_F_Tally
|
AR_F_Conclusion
|
ARI_F_Tally
|
ARI_F_Conclusion
|
ARIS_F_Tally
|
ARIS_F_Conclusion
|
winning_1
|
winning_2
|
winning_3
|
winning_4
|
winning_5
|
winning_6
|
winning_7
|
winning_8
|
winning_9
|
winning_10
|
winning_11
|
winning_12
|
|
700005895
|
JACK DANIELS BLK WHSKY 750M
|
700005895
|
4.786171e-12
|
1.665335e-15
|
not white noise
|
NA
|
0.01000000
|
0.01000000
|
inconclusive
|
9.0689744
|
9.1510256
|
9.1886325
|
9.1215385
|
9.2110256
|
9.6625641
|
9.9165934
|
10.1410897
|
10.2612821
|
10.3625641
|
10.2468298
|
10.6138462
|
2.5666667
|
2.5666667
|
2.5666667
|
2.5666667
|
2.5666667
|
2.5666667
|
2.5666667
|
2.5666667
|
2.5666667
|
2.5666667
|
2.5666667
|
2.5666667
|
5.6054255
|
5.9078848
|
6.4067013
|
6.4869790
|
6.5917304
|
6.6944584
|
7.0349089
|
7.1368006
|
7.1971954
|
7.2508134
|
7.1868458
|
7.4055501
|
2.9385459
|
6.1082771
|
3.1081098
|
6.2741873
|
3.2881512
|
5.0736059
|
3.5813577
|
4.8597982
|
3.4703213
|
4.3656519
|
3.5756278
|
4.1122127
|
5.5392257
|
5.9681069
|
6.3616927
|
6.4042988
|
6.4227636
|
6.4027409
|
6.6372491
|
6.7007490
|
6.7862477
|
6.7964345
|
6.7325565
|
6.8699152
|
2.93854588
|
6.1082771
|
3.1081098
|
6.2741873
|
3.2881512
|
5.0736059
|
3.5813577
|
4.8597982
|
3.4703213
|
4.3656519
|
3.5756278
|
4.1122127
|
6.0065094
|
6.4548964
|
6.6022233
|
6.7586774
|
6.8199332
|
6.6568282
|
6.9602557
|
6.9366006
|
6.9896175
|
6.9462730
|
6.8550545
|
6.7939120
|
3.436980899
|
6.5566763
|
3.6522630
|
7.0612788
|
4.1406292
|
6.026328
|
4.6235795
|
6.0845704
|
4.687679
|
5.7308138
|
4.9907181
|
5.6522099
|
8.5418681
|
9.2801912
|
8.6697046
|
9.3972474
|
9.5739507
|
9.0672141
|
9.6068875
|
9.4353998
|
9.5065170
|
9.6750977
|
9.3342680
|
9.3124904
|
3.43698090
|
6.5566763
|
3.6522630
|
7.0612788
|
4.1406292
|
6.0263278
|
4.6235795
|
6.0845704
|
4.6876795
|
5.7308138
|
4.9907181
|
5.6522099
|
8.7661541
|
9.803003
|
9.924557
|
10.2763219
|
10.4523866
|
10.4310370
|
10.511161
|
10.458360
|
10.511135
|
10.486019
|
10.452995
|
9.991491
|
1.8534557
|
4.438188e+00
|
4.832533e+00
|
2.064003e+00
|
2.975539e+00
|
6.009348e+00
|
9.964272e-01
|
5.001365e+00
|
2.999478e+00
|
8.000199442
|
0.99992378
|
9.0000291
|
9.155954
|
9.981319
|
10.046597
|
10.254467
|
10.473186
|
10.450372
|
10.530208
|
10.474697
|
10.512969
|
10.469637
|
10.440687
|
9.977978
|
2.6914828
|
3.9313527
|
4.955730
|
1.9714508
|
2.98158891
|
5.9881269
|
0.992343155
|
4.99506218
|
2.9968157
|
7.997946449
|
0.9986757
|
8.9991460
|
6.0498044
|
6.9827080
|
7.0426996
|
7.0088504
|
6.9382068
|
6.9870023
|
7.0209966
|
7.1040564
|
7.1329618
|
7.3064732
|
7.1409205
|
6.9999049
|
4.678167
|
5.3755667
|
5.9625333
|
3.1371000
|
4.6781667
|
6.3589000
|
2.8396333
|
6.0982667
|
4.6948333
|
6.6237000
|
2.8396333
|
6.6237000
|
7.9375735
|
11.7480491
|
12.2976824
|
11.3134571
|
11.3812936
|
12.6606764
|
13.2381222
|
13.3745233
|
13.6682289
|
13.9975748
|
14.0103835
|
14.5946817
|
2.538217702
|
0.97899051
|
1.2940198
|
2.3134472
|
1.6646977
|
1.09209151
|
1.1568677
|
1.4891501
|
1.2349146
|
1.1074768
|
1.1229634
|
1.2105442
|
3
|
4
|
4
|
4
|
5
|
8
|
0
|
4
|
3.0
|
3
|
5
|
11
|
13
|
Different
|
13
|
Different
|
7
|
Inconclusive
|
ARMA_1_ASE
|
AR_2_ASE
|
ARMA_3_ASE
|
ARMA_4_ASE
|
ARMA_5_ASE
|
ARMA_6_ASE
|
ARMA_7_ASE
|
ARMA_8_ASE
|
ARMA_9_ASE
|
ARMA_10_ASE
|
ARMA_11_ASE
|
ARI_12_ASE
|
|
700005448
|
TORTILLA SILVER TEQ DSS 1.75L
|
700005448
|
8.807377e-02
|
3.665865e-01
|
white noise
|
white noise
|
0.36870575
|
0.04762877
|
not stationary
|
0.5716556
|
0.5655017
|
0.5624248
|
0.5890915
|
0.6004761
|
0.6090060
|
0.6144394
|
0.6163350
|
0.6305729
|
0.6623222
|
0.6920285
|
0.7245188
|
1.7666667
|
1.7666667
|
1.7666667
|
1.7666667
|
1.7666667
|
1.7666667
|
1.7666667
|
1.7666667
|
1.7666667
|
1.7666667
|
1.7666667
|
1.7666667
|
0.6942115
|
0.6524714
|
0.6334266
|
0.6328648
|
0.6385497
|
0.6432841
|
0.6451130
|
0.6422000
|
0.6525317
|
0.6822315
|
0.7099566
|
0.7406985
|
1.3264075
|
1.8551896
|
1.7485388
|
2.1041247
|
1.5952620
|
1.8547966
|
1.8165794
|
1.8481962
|
1.7264328
|
1.8256658
|
1.7942109
|
1.7898796
|
0.7207688
|
0.6861532
|
0.6757951
|
0.6679848
|
0.6538848
|
0.6548755
|
0.6565445
|
0.6579988
|
0.6676573
|
0.6958380
|
0.7219311
|
0.7529246
|
1.32640746
|
1.8551896
|
1.7485388
|
2.1041247
|
1.5952620
|
1.8547966
|
1.8165794
|
1.8481962
|
1.7264328
|
1.8256658
|
1.7942109
|
1.7898796
|
0.7111108
|
0.6771035
|
0.6693311
|
0.6447135
|
0.6409545
|
0.6513507
|
0.6510615
|
0.6474969
|
0.6610099
|
0.6801487
|
0.7078773
|
0.7412894
|
1.483642804
|
1.6939099
|
1.8377815
|
2.0630840
|
1.8225134
|
1.744476
|
1.8927136
|
1.9130802
|
1.842049
|
1.8223885
|
1.8720112
|
1.8767798
|
0.6405925
|
0.6299260
|
0.6164646
|
0.6233314
|
0.6240543
|
0.6366802
|
0.6364920
|
0.6307053
|
0.6530211
|
0.6699885
|
0.6931830
|
0.7184782
|
1.75858628
|
1.2814051
|
2.3923115
|
1.6312175
|
2.3777548
|
1.1617182
|
2.5041757
|
1.5867593
|
2.0791025
|
1.7296408
|
1.9551375
|
2.0209115
|
1.1825183
|
1.156639
|
1.050869
|
0.9929159
|
0.9851617
|
0.9846825
|
0.993386
|
1.009069
|
1.014999
|
1.044005
|
1.057417
|
1.071727
|
1.3661099
|
1.853173e+00
|
2.515872e+00
|
1.392691e+00
|
1.793101e+00
|
2.130942e+00
|
1.244046e+00
|
3.060392e+00
|
9.889031e-01
|
2.110560127
|
1.08497928
|
3.0194668
|
2.032046
|
1.722801
|
1.651667
|
1.800139
|
2.099372
|
2.177397
|
2.211459
|
2.195750
|
2.284632
|
2.260631
|
2.206559
|
2.181600
|
2.6835456
|
0.4203508
|
3.786647
|
0.5895539
|
3.02059874
|
0.9706353
|
2.254651845
|
2.55529723
|
1.5109352
|
1.721572263
|
1.4321250
|
2.9702114
|
0.7128529
|
0.6605771
|
0.6448564
|
0.7148635
|
0.7476166
|
0.7601325
|
0.7766704
|
0.7946097
|
0.8120335
|
0.8453550
|
0.8714070
|
0.8945990
|
1.848867
|
1.8488667
|
1.8488667
|
1.0650667
|
1.8488667
|
1.8488667
|
1.0650667
|
2.7457000
|
1.0650667
|
1.8488667
|
1.0650667
|
2.7457000
|
0.5965944
|
0.6451668
|
0.6550854
|
0.6828447
|
0.6707437
|
0.6926139
|
0.7020487
|
0.7023441
|
0.7137983
|
0.7381717
|
0.7587158
|
0.7879413
|
1.656563945
|
2.32850043
|
1.5366885
|
1.6044749
|
1.8466523
|
1.54633712
|
1.5833060
|
1.6343155
|
1.5609408
|
1.5760818
|
1.5958171
|
1.5682539
|
3
|
2
|
2
|
3
|
2
|
2
|
2
|
2
|
0.0
|
0
|
1
|
3
|
0
|
Same
|
1
|
Same
|
0
|
Same
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
ARIMA_12_ASE
|
|
701001904
|
FLOR DE CANA GOLD RUM 4YR 1L
|
701001904
|
3.801776e-01
|
1.126704e-01
|
white noise
|
white noise
|
0.01799216
|
0.01000000
|
inconclusive
|
0.6995513
|
0.5290385
|
0.4687821
|
0.4425000
|
0.4523718
|
0.4589530
|
0.4816026
|
0.5017949
|
0.5197792
|
0.5367308
|
0.5501340
|
0.5606624
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.7495953
|
0.6475434
|
0.5938069
|
0.5412605
|
0.5349852
|
0.5315703
|
0.5433585
|
0.5522855
|
0.5613699
|
0.5713498
|
0.5802719
|
0.5876067
|
0.6189358
|
0.6527571
|
0.6497553
|
0.6500217
|
0.6499981
|
0.6500002
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
1.0442915
|
0.8301104
|
0.7067074
|
0.6343535
|
0.6128546
|
0.5994945
|
0.5999598
|
0.6006017
|
0.6010513
|
0.6102566
|
0.6137591
|
0.6163443
|
0.65000000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
0.6500000
|
1.0349494
|
0.8290190
|
0.7312359
|
0.6620074
|
0.6296827
|
0.6136809
|
0.6087270
|
0.6020128
|
0.6082493
|
0.6280958
|
0.6405428
|
0.6497845
|
1.000000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.000000
|
1.0000000
|
1.0000000
|
1.000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.9471137
|
1.3335527
|
1.1635218
|
1.0611714
|
1.0070012
|
1.0167857
|
0.9846006
|
1.0169535
|
1.0343591
|
1.0306570
|
1.0266886
|
1.0104301
|
1.00000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
2.3016359
|
2.160235
|
2.157709
|
2.1207569
|
2.0960530
|
2.0694459
|
2.093314
|
2.033312
|
1.987007
|
1.957459
|
1.933255
|
1.913106
|
3.0000000
|
1.942890e-17
|
-3.608225e-17
|
-3.041192e-17
|
1.000000e+00
|
1.000000e+00
|
1.942890e-17
|
1.000000e+00
|
1.000000e+00
|
1.000000000
|
1.00000000
|
1.0000000
|
1.772864
|
2.322617
|
2.302087
|
2.249213
|
2.201378
|
2.157702
|
2.153409
|
2.092362
|
2.041340
|
2.006400
|
1.978361
|
1.954473
|
2.9999502
|
0.0000000
|
0.000000
|
0.0000000
|
1.00000000
|
1.0000000
|
0.000000000
|
1.00000000
|
1.0000000
|
1.000000000
|
1.0000000
|
1.0000000
|
1.3000939
|
1.1570493
|
1.1074100
|
1.0702476
|
1.0412809
|
1.0112418
|
0.9684744
|
0.9486574
|
0.9534083
|
0.9542528
|
0.9717126
|
0.9804113
|
1.750733
|
0.2715000
|
0.2715000
|
0.2715000
|
1.0675000
|
1.0675000
|
0.2715000
|
1.0675000
|
1.0675000
|
1.0675000
|
1.0675000
|
1.0675000
|
1.2378788
|
1.0097607
|
0.9069398
|
0.7724612
|
0.8176093
|
0.8655840
|
0.8303112
|
0.8940488
|
0.8803867
|
0.8891547
|
0.8730491
|
0.8718788
|
-0.003686558
|
0.07880159
|
0.1416579
|
0.9675949
|
0.2774209
|
0.05252596
|
0.8467427
|
0.2028610
|
0.5669621
|
0.4753789
|
0.7164548
|
0.6459699
|
0
|
2
|
1
|
0
|
2
|
1
|
2
|
0
|
0.0
|
0
|
1
|
1
|
0
|
Same
|
0
|
Same
|
0
|
Same
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
|
700005925
|
CASA NOBLE CRYSTAL TEQ 6PK 750M
|
700005925
|
9.489966e-04
|
2.502709e-04
|
not white noise
|
white noise
|
0.56183917
|
0.10000000
|
inconclusive
|
3.1726068
|
3.2405556
|
2.7649145
|
2.5264530
|
2.3567094
|
2.2431197
|
2.1619841
|
2.0844658
|
2.0233191
|
1.9738889
|
1.9341453
|
1.8899145
|
1.3500000
|
1.3500000
|
1.3500000
|
1.3500000
|
1.3500000
|
1.3500000
|
1.3500000
|
1.3500000
|
1.3500000
|
1.3500000
|
1.3500000
|
1.3500000
|
4.5966191
|
4.3161665
|
3.4365422
|
3.1320838
|
2.7848548
|
2.5911752
|
2.4824384
|
2.3478787
|
2.2610588
|
2.1947776
|
2.1270249
|
2.0663430
|
0.7225347
|
0.8637380
|
1.1752617
|
1.0767431
|
1.1714654
|
1.2640872
|
1.2357247
|
1.2825245
|
1.3091095
|
1.3033956
|
1.3236393
|
1.3311831
|
4.6180807
|
4.3620508
|
3.4623764
|
3.1590933
|
2.8408239
|
2.6484569
|
2.5365775
|
2.3976202
|
2.3051311
|
2.2344015
|
2.1656202
|
2.1016788
|
0.72253469
|
0.8637380
|
1.1752617
|
1.0767431
|
1.1714654
|
1.2640872
|
1.2357247
|
1.2825245
|
1.3091095
|
1.3033956
|
1.3236393
|
1.3311831
|
5.7312374
|
5.0980361
|
4.1245420
|
3.6589359
|
3.2222853
|
3.1039759
|
2.8972426
|
2.6976785
|
2.6349594
|
2.4971475
|
2.4257860
|
2.3649153
|
0.003428975
|
0.2622852
|
0.5162665
|
0.2921546
|
0.2720958
|
0.400818
|
0.3365328
|
0.3104867
|
0.355351
|
0.3438979
|
0.3280937
|
0.3423912
|
5.7705099
|
5.0332742
|
4.0433952
|
3.6224390
|
3.1748973
|
3.0480544
|
2.8517410
|
2.6583122
|
2.6026693
|
2.4718678
|
2.3973343
|
2.3338023
|
-0.02714597
|
0.1918815
|
0.4429970
|
0.2198456
|
0.2550397
|
0.3166516
|
0.2686866
|
0.2734572
|
0.2882915
|
0.2780980
|
0.2784749
|
0.2819902
|
5.7135260
|
7.165921
|
7.422999
|
7.6199840
|
7.6454588
|
7.6091332
|
7.585894
|
7.541272
|
7.450487
|
7.374793
|
6.983284
|
6.654251
|
-2.4977111
|
6.729156e+00
|
-1.212209e+00
|
-1.273990e+00
|
2.624861e-01
|
-1.046592e+00
|
-3.388357e-01
|
-1.154433e-01
|
-6.015934e-01
|
-0.078887469
|
-0.22358983
|
0.7220991
|
6.311784
|
7.842069
|
8.196566
|
8.260550
|
8.124630
|
8.058478
|
7.944359
|
7.942456
|
7.808970
|
7.577955
|
7.185047
|
6.843732
|
-2.4977111
|
6.7291556
|
-1.212209
|
-1.2739896
|
0.26248613
|
-1.0465918
|
-0.338835725
|
-0.11544334
|
-0.6015934
|
-0.078887469
|
-0.2235898
|
0.7220991
|
5.5063671
|
6.4615421
|
6.8569428
|
6.9403913
|
6.8899397
|
6.6828534
|
6.4541674
|
6.3184049
|
6.0536568
|
5.8011695
|
5.5388071
|
5.3284908
|
1.259600
|
5.2485333
|
0.6839000
|
0.6839000
|
0.6839000
|
0.6839000
|
0.6839000
|
0.6839000
|
0.6839000
|
0.6839000
|
0.6839000
|
1.3386333
|
25.8647856
|
14.2830144
|
19.0885569
|
14.5709263
|
17.2296374
|
14.5200195
|
16.4182672
|
14.4743929
|
15.8403500
|
14.3436477
|
15.4783054
|
14.2539738
|
5.569774663
|
0.96383865
|
5.5458039
|
1.0589248
|
5.5445936
|
1.08410416
|
5.5445087
|
1.0884563
|
5.5445074
|
1.0892216
|
5.5445086
|
1.0893584
|
0
|
0
|
0
|
0
|
1
|
0
|
0
|
1
|
0.0
|
0
|
0
|
1
|
8
|
Inconclusive
|
8
|
Inconclusive
|
0
|
Same
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
|
701001770
|
PIRAS 51 CACHACA 80 1L
|
701001770
|
1.862330e-02
|
5.561031e-03
|
not white noise
|
white noise
|
0.21808191
|
0.07405623
|
inconclusive
|
0.7631624
|
0.7772650
|
0.7768376
|
0.7029060
|
0.6354701
|
0.6046154
|
0.6243346
|
0.6400855
|
0.6620228
|
0.6680342
|
0.6624631
|
0.6627350
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
0.6498729
|
0.6709398
|
0.6600289
|
0.6062765
|
0.5525729
|
0.5312124
|
0.5595709
|
0.5828176
|
0.6105208
|
0.6213706
|
0.6200620
|
0.6239249
|
1.0432794
|
1.0490967
|
1.0498786
|
1.0499837
|
1.0499978
|
1.0499997
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
0.6446346
|
0.6657276
|
0.6525417
|
0.5925254
|
0.5308812
|
0.5020173
|
0.5298103
|
0.5568825
|
0.5879643
|
0.6020638
|
0.6045947
|
0.6120855
|
1.05000000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
1.0500000
|
0.7803533
|
0.8929894
|
0.8497721
|
0.8027063
|
0.7675136
|
0.7567434
|
0.7859594
|
0.8179896
|
0.8364671
|
0.8647910
|
0.9235486
|
0.9779088
|
1.430652114
|
1.3249808
|
1.2159647
|
1.2516776
|
1.2945449
|
1.270597
|
1.2621083
|
1.2697373
|
1.272804
|
1.2690478
|
1.2688739
|
1.2699860
|
0.5001610
|
0.5698775
|
0.6161735
|
0.6000020
|
0.5748791
|
0.5755548
|
0.6115162
|
0.6488012
|
0.6920893
|
0.7273038
|
0.7643915
|
0.7972260
|
1.34886792
|
1.3488679
|
1.3488679
|
1.3488679
|
1.3488679
|
1.3488679
|
1.3488679
|
1.3488679
|
1.3488679
|
1.3488679
|
1.3488679
|
1.3488679
|
2.1299157
|
2.250523
|
2.379156
|
2.5211432
|
2.5465898
|
2.5717437
|
2.632695
|
2.668386
|
2.715570
|
2.727514
|
2.716594
|
2.652661
|
1.0000000
|
2.000000e+00
|
3.000000e+00
|
2.000000e+00
|
1.000000e+00
|
1.000000e+00
|
1.000000e+00
|
1.000000e+00
|
2.000000e+00
|
2.000000000
|
1.00000000
|
1.0000000
|
2.133433
|
2.262730
|
2.385876
|
2.528330
|
2.554339
|
2.586967
|
2.650574
|
2.684445
|
2.731844
|
2.739532
|
2.727685
|
2.665071
|
1.0000000
|
2.0000000
|
3.000000
|
2.0000000
|
1.00000000
|
1.0000000
|
1.000000000
|
1.00000000
|
2.0000000
|
2.000000000
|
1.0000000
|
1.0000000
|
1.5281369
|
1.5652216
|
1.7439732
|
1.8406330
|
1.7953375
|
1.7538955
|
1.7491473
|
1.7152193
|
1.7694161
|
1.8062429
|
1.7863637
|
1.7498799
|
1.655567
|
2.3852000
|
2.6609667
|
2.3852000
|
1.6555667
|
1.6555667
|
1.6555667
|
1.6555667
|
2.3852000
|
2.3852000
|
1.6555667
|
1.6555667
|
1.3086425
|
0.8759303
|
0.9172024
|
0.7938742
|
0.7221067
|
0.6717083
|
0.7000032
|
0.7194444
|
0.7387459
|
0.7402898
|
0.7304822
|
0.7326094
|
0.506872287
|
1.26454849
|
0.6144269
|
0.8672698
|
0.6422513
|
0.72214489
|
0.6497136
|
0.6719595
|
0.6517768
|
0.6577276
|
0.6523498
|
0.6540086
|
1
|
1
|
0
|
0
|
1
|
0
|
3
|
1
|
0.0
|
1
|
1
|
2
|
3
|
Same
|
2
|
Same
|
0
|
Same
|
ARIMA_1_ASE
|
ARIMA_2_ASE
|
ARIMA_3_ASE
|
ARMA_4_ASE
|
ARMA_5_ASE
|
ARMA_6_ASE
|
ARMA_7_ASE
|
ARMA_8_ASE
|
ARMA_9_ASE
|
ARMA_10_ASE
|
ARMA_11_ASE
|
ARMA_12_ASE
|
|
701001908
|
MCCORMICK VODKA 80 1.75L
|
701001908
|
6.426664e-08
|
6.568032e-10
|
not white noise
|
NA
|
0.35666834
|
0.01740497
|
not stationary
|
2.2285256
|
2.4682692
|
2.0174145
|
1.8387821
|
1.7351923
|
1.6960470
|
1.6831044
|
1.6737179
|
1.6755342
|
1.6782692
|
1.6840035
|
1.6919872
|
1.5166667
|
1.5166667
|
1.5166667
|
1.5166667
|
1.5166667
|
1.5166667
|
1.5166667
|
1.5166667
|
1.5166667
|
1.5166667
|
1.5166667
|
1.5166667
|
2.4832483
|
2.7884601
|
2.5526392
|
2.4921262
|
2.4662996
|
2.4860781
|
2.4224876
|
2.3582667
|
2.3111059
|
2.2122487
|
2.1354672
|
2.0701868
|
1.3401307
|
1.1609156
|
2.2917969
|
1.4780675
|
1.2880298
|
1.9169957
|
1.5267468
|
1.3762987
|
1.7208581
|
1.5387184
|
1.4333333
|
1.6194098
|
2.6388001
|
2.9401760
|
2.8495561
|
2.5864904
|
2.3975025
|
2.3198086
|
2.2665143
|
2.2897757
|
2.3101477
|
2.2679359
|
2.1984358
|
2.1269990
|
1.34013071
|
1.1609156
|
2.2917969
|
1.4780675
|
1.2880298
|
1.9169957
|
1.5267468
|
1.3762987
|
1.7208581
|
1.5387184
|
1.4333333
|
1.6194098
|
2.8287590
|
3.0922478
|
2.9173888
|
3.0093555
|
3.0584183
|
3.1341009
|
3.0941062
|
2.9857088
|
2.9088922
|
2.7147794
|
2.5400621
|
2.4206506
|
1.419774355
|
1.2906208
|
2.4812131
|
1.6294789
|
1.4822995
|
2.185309
|
1.7312375
|
1.6057336
|
2.017685
|
1.7786510
|
1.6837401
|
1.9233905
|
2.8193328
|
3.1843995
|
3.1282574
|
3.0207945
|
2.9848679
|
3.0366704
|
2.9866854
|
2.8984773
|
2.8083638
|
2.6253766
|
2.4693641
|
2.3587800
|
1.01860624
|
1.0478045
|
2.2427166
|
1.4686003
|
1.2267672
|
1.8538817
|
1.6050905
|
1.3785051
|
1.6742643
|
1.6258436
|
1.4753673
|
1.6002531
|
2.7180753
|
2.731890
|
2.603679
|
2.3893369
|
2.2532880
|
2.2452744
|
2.215078
|
2.217877
|
2.233337
|
2.231591
|
2.235785
|
2.232764
|
0.6407034
|
5.500378e+00
|
1.791860e+00
|
7.559485e-01
|
9.272458e-01
|
2.295159e+00
|
8.820483e-01
|
2.140238e-02
|
1.105101e+00
|
0.953093899
|
1.02782941
|
3.0364945
|
2.718075
|
2.731890
|
2.603679
|
2.389337
|
2.253288
|
2.245274
|
2.215078
|
2.217877
|
2.233337
|
2.231591
|
2.235785
|
2.232764
|
0.6407034
|
5.5003778
|
1.791860
|
0.7559485
|
0.92724581
|
2.2951594
|
0.882048312
|
0.02140238
|
1.1051015
|
0.953093899
|
1.0278294
|
3.0364945
|
2.0267153
|
1.8918195
|
1.7548877
|
1.6352237
|
1.6274658
|
1.6535894
|
1.6483592
|
1.6804013
|
1.6895730
|
1.6768923
|
1.6980926
|
1.6920025
|
1.060400
|
4.0770667
|
1.0604000
|
1.0604000
|
1.0604000
|
1.8818667
|
1.0604000
|
0.4446000
|
1.0604000
|
1.0604000
|
1.0604000
|
2.8422667
|
2.0697694
|
10.0793992
|
7.5857859
|
6.0418036
|
6.8814292
|
6.0415395
|
5.4113769
|
7.4222835
|
6.7802824
|
6.2774426
|
6.6811277
|
6.2740434
|
1.535367414
|
6.00781067
|
0.4133082
|
1.3716830
|
4.4929023
|
0.94921962
|
1.5348301
|
6.2202014
|
1.4905833
|
1.3581906
|
4.2693955
|
1.4464008
|
2
|
4
|
3
|
0
|
2
|
0
|
0
|
2
|
0.0
|
1
|
2
|
2
|
2
|
Same
|
2
|
Same
|
0
|
Same
|
RF_1_ASE
|
RF_2_ASE
|
RF_3_ASE
|
RF_4_ASE
|
RF_5_ASE
|
RF_6_ASE
|
RF_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
RF_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
|
700005926
|
MCCORMICK VODKA 80 TRVL 750M
|
700005926
|
2.934075e-10
|
3.874856e-11
|
not white noise
|
NA
|
0.07556121
|
0.10000000
|
inconclusive
|
0.6716239
|
0.5062393
|
0.4528205
|
0.4171368
|
0.3767521
|
0.3481197
|
0.3262027
|
0.3161752
|
0.3018234
|
0.2911111
|
0.2919037
|
0.2927778
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
0.6182281
|
0.4551938
|
0.4304193
|
0.4188211
|
0.3983483
|
0.3700911
|
0.3485136
|
0.3334866
|
0.3165183
|
0.3033057
|
0.3013792
|
0.2992889
|
1.3711375
|
1.1760882
|
1.2549802
|
1.2993734
|
1.2362031
|
1.1794933
|
1.2001215
|
1.1423049
|
1.1184965
|
1.1244998
|
1.1167234
|
1.1133609
|
0.5968163
|
0.4579625
|
0.4497181
|
0.4511265
|
0.4286348
|
0.4049576
|
0.3825204
|
0.3637018
|
0.3428458
|
0.3270631
|
0.3227980
|
0.3181758
|
1.37113745
|
1.1760882
|
1.2549802
|
1.2993734
|
1.2362031
|
1.1794933
|
1.2001215
|
1.1423049
|
1.1184965
|
1.1244998
|
1.1167234
|
1.1133609
|
0.6994756
|
0.5598007
|
0.5893693
|
0.6548900
|
0.6911132
|
0.6870577
|
0.6978623
|
0.7013999
|
0.6918955
|
0.6905217
|
0.6916737
|
0.6876350
|
1.000000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.000000
|
1.0000000
|
1.0000000
|
1.000000
|
1.0000000
|
1.0000000
|
1.0000000
|
0.7227470
|
0.5716485
|
0.6012171
|
0.6667377
|
0.7006763
|
0.6931936
|
0.7031823
|
0.7046800
|
0.6948582
|
0.6932306
|
0.6941748
|
0.6899631
|
1.00000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
2.0011501
|
1.388384
|
1.393567
|
1.2746320
|
1.1727551
|
1.0914126
|
1.030270
|
1.024978
|
1.018166
|
1.005870
|
1.003606
|
1.002454
|
3.0000000
|
1.000000e+00
|
2.000000e+00
|
2.000000e+00
|
1.000000e+00
|
-2.386980e-16
|
1.000000e+00
|
-2.664535e-16
|
1.000000e+00
|
1.000000000
|
1.00000000
|
1.0000000
|
2.242084
|
1.956935
|
2.049454
|
1.863884
|
1.727736
|
1.549117
|
1.558636
|
1.507911
|
1.542104
|
1.495994
|
1.398345
|
1.408520
|
3.0000000
|
1.0000000
|
2.000000
|
2.0000000
|
1.00000000
|
0.0000000
|
1.000000000
|
0.00000000
|
1.0000000
|
1.000000000
|
1.0000000
|
1.0000000
|
1.2265354
|
0.9759582
|
0.8768046
|
0.7840333
|
0.7018179
|
0.6620587
|
0.6297858
|
0.6420082
|
0.6422437
|
0.6392397
|
0.6457699
|
0.6497929
|
2.286700
|
0.9794333
|
1.8411667
|
1.8411667
|
0.9794333
|
0.1454333
|
0.9794333
|
0.1454333
|
0.9794333
|
0.9794333
|
0.9794333
|
0.9794333
|
0.6084604
|
0.4682055
|
0.4123026
|
0.4405396
|
0.4195244
|
0.3984964
|
0.3871777
|
0.3772918
|
0.3850468
|
0.3739793
|
0.3805600
|
0.3832535
|
1.007112861
|
1.14212264
|
1.0166948
|
1.4969534
|
0.5846710
|
1.42631843
|
0.8403497
|
1.2238527
|
0.9389953
|
1.9732302
|
1.4297120
|
1.7914312
|
1
|
1
|
1
|
2
|
1
|
1
|
2
|
2
|
1.0
|
1
|
0
|
1
|
3
|
Same
|
0
|
Same
|
0
|
Same
|
ARMA_1_ASE
|
AR_2_ASE
|
MLP_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
|
701001850
|
RICH & RARE CANADIAN RSV 6PK 750M
|
701001850
|
6.360152e-01
|
8.443310e-01
|
white noise
|
white noise
|
0.03437467
|
0.10000000
|
stationary
|
11.9384353
|
10.4039481
|
9.9608284
|
10.6224737
|
10.8606404
|
11.0467686
|
11.1601569
|
11.2330186
|
11.2435663
|
11.3251840
|
11.5126614
|
11.6416917
|
4.1616667
|
4.1616667
|
4.1616667
|
4.1616667
|
4.1616667
|
4.1616667
|
4.1616667
|
4.1616667
|
4.1616667
|
4.1616667
|
4.1616667
|
4.1616667
|
11.4489409
|
10.1784232
|
9.8099232
|
10.5092017
|
10.7700050
|
10.9712374
|
11.0954158
|
11.1763701
|
11.1932121
|
11.2798652
|
11.4714625
|
11.6039260
|
4.1798794
|
4.1596149
|
4.1618978
|
4.1616406
|
4.1616696
|
4.1616663
|
4.1616667
|
4.1616667
|
4.1616667
|
4.1616667
|
4.1616667
|
4.1616667
|
10.9581620
|
10.7792516
|
11.0698208
|
12.0680550
|
12.0926064
|
12.3485626
|
12.1985500
|
12.0879789
|
12.0085145
|
12.0304265
|
12.1421225
|
12.2456236
|
5.80614173
|
2.8852428
|
5.0417073
|
3.5549156
|
4.5799961
|
3.8732461
|
4.3605205
|
4.0245653
|
4.2561923
|
4.0964952
|
4.2065997
|
4.1306873
|
13.0951858
|
11.6481072
|
11.1661089
|
12.4410030
|
13.0870605
|
12.8400163
|
12.9485012
|
13.0177933
|
12.7570510
|
12.8400437
|
12.8687104
|
12.6773155
|
3.780987287
|
1.8499237
|
3.1462242
|
2.8342741
|
3.0518430
|
3.021921
|
2.6582298
|
2.9916366
|
2.944486
|
2.8983373
|
2.9180147
|
2.8677563
|
15.9706679
|
14.2314006
|
12.0051347
|
14.2947521
|
14.6930887
|
14.8401812
|
14.7715397
|
15.1490428
|
14.9630577
|
15.3793237
|
15.4024611
|
15.4003271
|
0.58909996
|
1.0023861
|
1.0023861
|
1.0023861
|
1.0023861
|
1.0023861
|
1.0023861
|
1.0023861
|
1.0023861
|
1.0023861
|
1.0023861
|
1.0023861
|
16.4562445
|
18.766613
|
20.152105
|
20.9217712
|
19.5220324
|
18.6401552
|
17.977276
|
17.364732
|
16.904462
|
16.728555
|
16.479735
|
16.137770
|
11.0127590
|
1.627930e-04
|
5.000002e+00
|
9.000000e+00
|
3.381346e-10
|
2.000000e+00
|
5.000000e+00
|
7.000000e+00
|
4.997449e-17
|
4.000000000
|
1.00000000
|
4.0000000
|
15.974995
|
19.457766
|
19.705139
|
19.398040
|
17.880262
|
17.076971
|
16.550790
|
16.126107
|
15.784831
|
15.735607
|
15.589212
|
15.317253
|
11.0000000
|
0.0000000
|
5.000000
|
9.0000000
|
0.00000000
|
2.0000000
|
5.000000000
|
7.00000000
|
0.0000000
|
4.000000000
|
1.0000000
|
4.0000000
|
12.1384838
|
11.7215436
|
12.5130330
|
13.9877554
|
14.0829582
|
14.1292931
|
14.6592910
|
14.5272090
|
14.3196426
|
14.1653784
|
14.4699477
|
14.3462932
|
9.747967
|
1.5193000
|
5.4709300
|
7.1142467
|
1.5193000
|
2.8831133
|
5.1852767
|
6.5627467
|
1.5193000
|
4.2841967
|
1.8674333
|
4.2841967
|
11.7961948
|
10.4502693
|
10.0032988
|
10.7951898
|
11.0197483
|
11.1689675
|
11.2496409
|
11.3088068
|
11.2875094
|
11.3961481
|
11.5744275
|
11.6957635
|
3.629845634
|
3.59280893
|
3.5929578
|
3.5929568
|
3.5929568
|
3.59295678
|
3.5929568
|
3.5929568
|
3.5929568
|
3.5929568
|
3.5929568
|
3.5929568
|
6
|
8
|
0
|
11
|
0
|
0
|
4
|
5
|
1.7
|
9
|
0
|
2
|
0
|
Same
|
0
|
Same
|
0
|
Same
|
ARMA_1_ASE
|
AR_2_ASE
|
AR_3_ASE
|
AR_4_ASE
|
AR_5_ASE
|
AR_6_ASE
|
AR_7_ASE
|
AR_8_ASE
|
AR_9_ASE
|
AR_10_ASE
|
AR_11_ASE
|
AR_12_ASE
|
|
700005900
|
1800 SILVER TEQ 750M
|
700005900
|
6.656046e-02
|
2.436308e-04
|
inconclusive
|
white noise
|
0.01000000
|
0.01000000
|
inconclusive
|
1.1171368
|
1.1107265
|
1.1102991
|
1.1081624
|
1.1017521
|
1.1201282
|
1.0706166
|
1.0338034
|
1.0060256
|
0.9840598
|
0.9670202
|
0.9756838
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
1.2694401
|
1.2353152
|
1.2111815
|
1.2005473
|
1.1928079
|
1.1889463
|
1.1321560
|
1.0856273
|
1.0529158
|
1.0258907
|
1.0064240
|
1.0109016
|
0.1864625
|
0.7794505
|
0.5855561
|
0.6489555
|
0.6282252
|
0.6350036
|
0.6327872
|
0.6335119
|
0.6332749
|
0.6333524
|
0.6333271
|
0.6333354
|
1.3180946
|
1.2112054
|
1.1772851
|
1.1584018
|
1.1419437
|
1.1536212
|
1.0993249
|
1.0589231
|
1.0283543
|
1.0041556
|
0.9852891
|
0.9924302
|
-0.05591195
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.6333333
|
0.9557315
|
1.0151942
|
1.0146379
|
1.0069576
|
0.9392117
|
0.9051722
|
0.8653613
|
0.8297796
|
0.8010411
|
0.7715015
|
0.7554179
|
0.7541395
|
0.268686968
|
1.2008117
|
1.0003608
|
0.9785906
|
1.5935426
|
1.133580
|
0.7655984
|
1.2496301
|
1.076164
|
1.1593790
|
1.2569435
|
1.0189189
|
1.5081476
|
1.4503722
|
1.2940762
|
1.3000560
|
1.1782780
|
1.1161573
|
1.0581466
|
1.0076505
|
0.9827825
|
0.9540515
|
0.9299877
|
0.9175731
|
0.64265601
|
0.9245660
|
0.8978135
|
0.8021449
|
1.3182205
|
1.5003782
|
1.1435372
|
0.9864337
|
0.9272725
|
0.9557136
|
1.1530119
|
1.2810937
|
0.9574296
|
1.184131
|
1.377686
|
1.4540277
|
1.5115846
|
1.5065743
|
1.469295
|
1.438855
|
1.416015
|
1.397798
|
1.410954
|
1.402502
|
0.5151459
|
-2.653753e-01
|
1.367070e-01
|
1.929576e+00
|
3.627866e-02
|
2.981311e+00
|
9.627461e-03
|
9.950405e-01
|
1.002555e+00
|
-0.001316141
|
2.00067800
|
1.9996507
|
1.240651
|
1.421723
|
1.520543
|
1.556107
|
1.579133
|
1.557323
|
1.510084
|
1.475001
|
1.447865
|
1.426175
|
1.436418
|
1.425711
|
0.5151459
|
-0.2653753
|
0.136707
|
1.9295759
|
0.03627866
|
2.9813112
|
0.009627461
|
0.99504045
|
1.0025549
|
-0.001316141
|
2.0006780
|
1.9996507
|
1.0889227
|
1.1938315
|
1.2078672
|
1.1971481
|
1.1526314
|
1.1048728
|
1.0634745
|
1.0395780
|
1.0151023
|
0.9866821
|
0.9944546
|
0.9601414
|
0.925900
|
0.9259000
|
0.9259000
|
2.2016333
|
0.9259000
|
2.2636333
|
0.9259000
|
1.8691667
|
1.8691667
|
0.9259000
|
2.2016333
|
2.2016333
|
1.7626608
|
1.3982429
|
1.3505134
|
1.2944042
|
1.2719159
|
1.2784103
|
1.2171923
|
1.1711803
|
1.1363217
|
1.1076894
|
1.0844674
|
1.0909853
|
0.071294378
|
0.65838041
|
0.4123800
|
0.5241567
|
0.4754183
|
0.49742335
|
0.4873554
|
0.4919707
|
0.4898566
|
0.4908254
|
0.4903815
|
0.4905849
|
1
|
1
|
1
|
1
|
2
|
2
|
1
|
1
|
1.0
|
1
|
0
|
3
|
0
|
Same
|
8
|
Inconclusive
|
0
|
Same
|
ARI_1_ASE
|
ARI_2_ASE
|
ARI_3_ASE
|
ARI_4_ASE
|
ARI_5_ASE
|
ARI_6_ASE
|
ARI_7_ASE
|
ARI_8_ASE
|
ARI_9_ASE
|
ARI_10_ASE
|
ARI_11_ASE
|
ARI_12_ASE
|
|
701001907
|
FIREBALL CINN WHSKY NL 1.75L
|
701001907
|
2.684747e-02
|
5.086310e-03
|
not white noise
|
white noise
|
0.04668965
|
0.01000000
|
inconclusive
|
3.4168803
|
3.3079060
|
2.9254274
|
2.7130342
|
2.5886752
|
2.5040598
|
2.4198107
|
2.3614316
|
2.3071937
|
2.2732906
|
2.2324981
|
2.2626068
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
3.2898172
|
3.2366595
|
2.8775217
|
2.6771531
|
2.5599846
|
2.4801535
|
2.3993198
|
2.3435020
|
2.2912563
|
2.2589469
|
2.2194583
|
2.2506538
|
1.0668640
|
1.6835982
|
1.5868914
|
1.6020555
|
1.5996777
|
1.6000505
|
1.5999921
|
1.6000012
|
1.5999998
|
1.6000000
|
1.6000000
|
1.6000000
|
3.7157148
|
3.4365654
|
3.1173311
|
2.9231418
|
2.8139887
|
2.7461842
|
2.6322659
|
2.5592011
|
2.4549392
|
2.3839704
|
2.3220255
|
2.3329799
|
1.60000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
1.6000000
|
4.3497572
|
4.2915776
|
4.1116214
|
4.0215718
|
3.8129812
|
3.8353961
|
3.6344605
|
3.4995624
|
3.3521435
|
3.1619863
|
3.1006578
|
3.0391359
|
1.546153990
|
2.1375851
|
2.8225759
|
1.9654009
|
2.8285898
|
2.225588
|
2.3534364
|
2.5281922
|
2.309131
|
2.4762238
|
2.3792225
|
2.3964842
|
5.8000703
|
5.1632613
|
4.7546005
|
4.5020618
|
4.4388272
|
4.0885904
|
3.9722412
|
3.9131948
|
3.7770898
|
3.7799643
|
3.7415046
|
3.8206163
|
1.54615399
|
2.1375851
|
2.8225759
|
1.9654009
|
2.8285898
|
2.2255880
|
2.3534364
|
2.5281922
|
2.3091314
|
2.4762238
|
2.3792225
|
2.3964842
|
3.7581676
|
4.541867
|
4.310137
|
4.2013851
|
4.0199866
|
3.8782900
|
3.797198
|
3.763896
|
3.745683
|
3.701350
|
3.610695
|
3.476131
|
2.9189758
|
4.426907e+00
|
3.427754e-01
|
-3.060416e-01
|
3.175670e+00
|
7.106078e-02
|
9.099376e-01
|
2.376254e-02
|
1.970290e+00
|
3.014569540
|
0.01796634
|
4.9892791
|
3.409515
|
4.526630
|
5.475029
|
6.034526
|
5.773231
|
5.414569
|
5.320162
|
5.250294
|
5.147824
|
4.962384
|
4.771355
|
4.783543
|
2.5322567
|
5.0000000
|
0.000000
|
0.0000000
|
3.00000000
|
0.0000000
|
1.000000000
|
0.00000000
|
2.0000000
|
3.000000000
|
0.0000000
|
5.0000000
|
3.1179416
|
3.4747325
|
3.3036057
|
3.3439456
|
3.1473544
|
3.0340253
|
2.9631910
|
2.8960751
|
2.8821119
|
2.8307555
|
2.7583684
|
2.6500130
|
2.994033
|
3.8158667
|
0.5009333
|
0.5009333
|
2.9940333
|
0.5009333
|
1.0086333
|
0.5009333
|
2.1698000
|
2.9940333
|
0.5009333
|
3.8158667
|
3.2041042
|
3.7331123
|
4.1548037
|
3.8221708
|
3.8675977
|
4.0147802
|
4.0880622
|
4.1436985
|
4.2124801
|
4.2932663
|
4.5847800
|
4.5473579
|
1.726126015
|
2.51047722
|
2.8769059
|
2.0924927
|
2.6680562
|
2.88333626
|
3.1890527
|
3.6491556
|
3.5744128
|
3.2024774
|
4.2617630
|
3.8282143
|
1
|
2
|
1
|
1
|
3
|
0
|
2
|
2
|
1.0
|
3
|
1
|
5
|
1
|
Same
|
0
|
Same
|
0
|
Same
|
RF_1_ASE
|
AR_2_ASE
|
AR_3_ASE
|
AR_4_ASE
|
AR_5_ASE
|
AR_6_ASE
|
AR_7_ASE
|
AR_8_ASE
|
AR_9_ASE
|
AR_10_ASE
|
AR_11_ASE
|
AR_12_ASE
|
Forecast Aggregation by Product
# Filter dataset to only 1 product
df_taaka = df[df$Product == "TAAKA VODKA 80 1L",]
combinations0 = as.data.frame(df_taaka %>% group_by(Product_Type, Product,Customer_ID) %>% tally(sort=TRUE))
combinations = combinations0 %>% filter(n >= 42)
combinations_mean = combinations0 %>% filter(n < 42)
# Find data from file for first combination
all_dates = data.frame(date=c("1/1/2013","2/1/2013","3/1/2013","4/1/2013","5/1/2013","6/1/2013","7/1/2013","8/1/2013","9/1/2013","10/1/2013","11/1/2013","12/1/2013",
"1/1/2014","2/1/2014","3/1/2014","4/1/2014","5/1/2014","6/1/2014","7/1/2014","8/1/2014","9/1/2014","10/1/2014","11/1/2014","12/1/2014",
"1/1/2015","2/1/2015","3/1/2015","4/1/2015","5/1/2015","6/1/2015","7/1/2015","8/1/2015","9/1/2015","10/1/2015","11/1/2015","12/1/2015",
"1/1/2016","2/1/2016","3/1/2016","4/1/2016","5/1/2016","6/1/2016","7/1/2016","8/1/2016","9/1/2016","10/1/2016","11/1/2016","12/1/2016",
"1/1/2017","2/1/2017","3/1/2017","4/1/2017","5/1/2017","6/1/2017","7/1/2017","8/1/2017","9/1/2017","10/1/2017","11/1/2017","12/1/2017",
"1/1/2018","2/1/2018","3/1/2018","4/1/2018","5/1/2018","6/1/2018","7/1/2018","8/1/2018","9/1/2018","10/1/2018","11/1/2018","12/1/2018",
"1/1/2019","2/1/2019","3/1/2019","4/1/2019","5/1/2019","6/1/2019","7/1/2019","8/1/2019","9/1/2019","10/1/2019","11/1/2019","12/1/2019"))
date_combinations = merge(all_dates,combinations,all=TRUE)
date_combinations_mean = merge(all_dates,combinations_mean,all=TRUE)
str(date_combinations)
'data.frame': 2436 obs. of 5 variables:
$ date : Factor w/ 84 levels "1/1/2013","1/1/2014",..: 1 29 36 43 50 57 64 71 78 8 ...
$ Product_Type: Factor w/ 57 levels "ABSINTHE","AMARETTO",..: 49 49 49 49 49 49 49 49 49 49 ...
$ Product : Factor w/ 4017 levels "1800 ANEJO TEQ 6PK 750M",..: 2325 2325 2325 2325 2325 2325 2325 2325 2325 2325 ...
$ Customer_ID : int 700005925 700005925 700005925 700005925 700005925 700005925 700005925 700005925 700005925 700005925 ...
$ n : int 84 84 84 84 84 84 84 84 84 84 ...
'data.frame': 2436 obs. of 7 variables:
$ date : chr "1/1/2013" "2/1/2013" "3/1/2013" "4/1/2013" ...
$ Product_Type: Factor w/ 57 levels "ABSINTHE","AMARETTO",..: 49 49 49 49 49 49 49 49 49 49 ...
$ Product : Factor w/ 4017 levels "1800 ANEJO TEQ 6PK 750M",..: 2325 2325 2325 2325 2325 2325 2325 2325 2325 2325 ...
$ Customer_ID : int 700005925 700005925 700005925 700005925 700005925 700005925 700005925 700005925 700005925 700005925 ...
$ n : int 84 84 84 84 84 84 84 84 84 84 ...
$ STD_Cases : num 53.8 58 67 68 72 66 72 72 44 49.8 ...
$ Dollar_Sales: num 3135 3379 3903 3961 4194 ...
'data.frame': 336 obs. of 7 variables:
$ date : chr "1/1/2013" "2/1/2013" "3/1/2013" "4/1/2013" ...
$ Product_Type: Factor w/ 57 levels "ABSINTHE","AMARETTO",..: 49 49 49 49 49 49 49 49 49 49 ...
$ Product : Factor w/ 4017 levels "1800 ANEJO TEQ 6PK 750M",..: 2325 2325 2325 2325 2325 2325 2325 2325 2325 2325 ...
$ Customer_ID : int 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 ...
$ n : int 39 39 39 39 39 39 39 39 39 39 ...
$ STD_Cases : num 2 0 0 1 1 0 0 0 0 0 ...
$ Dollar_Sales: num 117 0 0 58 58 0 0 0 0 0 ...
# this loop is for forcasting all customers individually for one product
z = nrow(combinations)
results <- data.frame(Product_Type=integer(),
Product=character(),
Customer=integer(),
ljung_10=double(),
ljung_24=double(),
ljung_results=character(),
top_5_bic=character(),
ADF=double(),
KPSS=double(),
stationarity_results=character(),
EqualMeans_1_ASE=double(),
EqualMeans_2_ASE=double(),
EqualMeans_3_ASE=double(),
EqualMeans_4_ASE=double(),
EqualMeans_5_ASE=double(),
EqualMeans_6_ASE=double(),
EqualMeans_7_ASE=double(),
EqualMeans_8_ASE=double(),
EqualMeans_9_ASE=double(),
EqualMeans_10_ASE=double(),
EqualMeans_11_ASE=double(),
EqualMeans_12_ASE=double(),
EqualMeans_F1=double(),
EqualMeans_F2=double(),
EqualMeans_F3=double(),
EqualMeans_F4=double(),
EqualMeans_F5=double(),
EqualMeans_F6=double(),
EqualMeans_F7=double(),
EqualMeans_F8=double(),
EqualMeans_F9=double(),
EqualMeans_F10=double(),
EqualMeans_F11=double(),
EqualMeans_F12=double(),
AR_1_ASE=double(),
AR_2_ASE=double(),
AR_3_ASE=double(),
AR_4_ASE=double(),
AR_5_ASE=double(),
AR_6_ASE=double(),
AR_7_ASE=double(),
AR_8_ASE=double(),
AR_9_ASE=double(),
AR_10_ASE=double(),
AR_11_ASE=double(),
AR_12_ASE=double(),
AR_F1=double(),
AR_F2=double(),
AR_F3=double(),
AR_F4=double(),
AR_F5=double(),
AR_F6=double(),
AR_F7=double(),
AR_F8=double(),
AR_F9=double(),
AR_F10=double(),
AR_F11=double(),
AR_F12=double(),
ARMA_1_ASE=double(),
ARMA_2_ASE=double(),
ARMA_3_ASE=double(),
ARMA_4_ASE=double(),
ARMA_5_ASE=double(),
ARMA_6_ASE=double(),
ARMA_7_ASE=double(),
ARMA_8_ASE=double(),
ARMA_9_ASE=double(),
ARMA_10_ASE=double(),
ARMA_11_ASE=double(),
ARMA_12_ASE=double(),
ARMA_F1=double(),
ARMA_F2=double(),
ARMA_F3=double(),
ARMA_F4=double(),
ARMA_F5=double(),
ARMA_F6=double(),
ARMA_F7=double(),
ARMA_F8=double(),
ARMA_F9=double(),
ARMA_F10=double(),
ARMA_F11=double(),
ARMA_F12=double(),
ARI_1_ASE=double(),
ARI_2_ASE=double(),
ARI_3_ASE=double(),
ARI_4_ASE=double(),
ARI_5_ASE=double(),
ARI_6_ASE=double(),
ARI_7_ASE=double(),
ARI_8_ASE=double(),
ARI_9_ASE=double(),
ARI_10_ASE=double(),
ARI_11_ASE=double(),
ARI_12_ASE=double(),
ARI_F1=double(),
ARI_F2=double(),
ARI_F3=double(),
ARI_F4=double(),
ARI_F5=double(),
ARI_F6=double(),
ARI_F7=double(),
ARI_F8=double(),
ARI_F9=double(),
ARI_F10=double(),
ARI_F11=double(),
ARI_F12=double(),
ARIMA_1_ASE=double(),
ARIMA_2_ASE=double(),
ARIMA_3_ASE=double(),
ARIMA_4_ASE=double(),
ARIMA_5_ASE=double(),
ARIMA_6_ASE=double(),
ARIMA_7_ASE=double(),
ARIMA_8_ASE=double(),
ARIMA_9_ASE=double(),
ARIMA_10_ASE=double(),
ARIMA_11_ASE=double(),
ARIMA_12_ASE=double(),
ARIMA_F1=double(),
ARIMA_F2=double(),
ARIMA_F3=double(),
ARIMA_F4=double(),
ARIMA_F5=double(),
ARIMA_F6=double(),
ARIMA_F7=double(),
ARIMA_F8=double(),
ARIMA_F9=double(),
ARIMA_F10=double(),
ARIMA_F11=double(),
ARIMA_F12=double(),
ARI_S12_1_ASE=double(),
ARI_S12_2_ASE=double(),
ARI_S12_3_ASE=double(),
ARI_S12_4_ASE=double(),
ARI_S12_5_ASE=double(),
ARI_S12_6_ASE=double(),
ARI_S12_7_ASE=double(),
ARI_S12_8_ASE=double(),
ARI_S12_9_ASE=double(),
ARI_S12_10_ASE=double(),
ARI_S12_11_ASE=double(),
ARI_S12_12_ASE=double(),
ARI_S12_F1=double(),
ARI_S12_F2=double(),
ARI_S12_F3=double(),
ARI_S12_F4=double(),
ARI_S12_F5=double(),
ARI_S12_F6=double(),
ARI_S12_F7=double(),
ARI_S12_F8=double(),
ARI_S12_F9=double(),
ARI_S12_F10=double(),
ARI_S12_F11=double(),
ARI_S12_F12=double(),
ARIMA_S12_1_ASE=double(),
ARIMA_S12_2_ASE=double(),
ARIMA_S12_3_ASE=double(),
ARIMA_S12_4_ASE=double(),
ARIMA_S12_5_ASE=double(),
ARIMA_S12_6_ASE=double(),
ARIMA_S12_7_ASE=double(),
ARIMA_S12_8_ASE=double(),
ARIMA_S12_9_ASE=double(),
ARIMA_S12_10_ASE=double(),
ARIMA_S12_11_ASE=double(),
ARIMA_S12_12_ASE=double(),
ARIMA_S12_F1=double(),
ARIMA_S12_F2=double(),
ARIMA_S12_F3=double(),
ARIMA_S12_F4=double(),
ARIMA_S12_F5=double(),
ARIMA_S12_F6=double(),
ARIMA_S12_F7=double(),
ARIMA_S12_F8=double(),
ARIMA_S12_F9=double(),
ARIMA_S12_F10=double(),
ARIMA_S12_F11=double(),
ARIMA_S12_F12=double(),
RF_1_ASE=double(),
RF_2_ASE=double(),
RF_3_ASE=double(),
RF_4_ASE=double(),
RF_5_ASE=double(),
RF_6_ASE=double(),
RF_7_ASE=double(),
RF_8_ASE=double(),
RF_9_ASE=double(),
RF_10_ASE=double(),
RF_11_ASE=double(),
RF_12_ASE=double(),
RF_F1=double(),
RF_F2=double(),
RF_F3=double(),
RF_F4=double(),
RF_F5=double(),
RF_F6=double(),
RF_F7=double(),
RF_F8=double(),
RF_F9=double(),
RF_F10=double(),
RF_F11=double(),
RF_F12=double(),
MLP_1_ASE=double(),
MLP_2_ASE=double(),
MLP_3_ASE=double(),
MLP_4_ASE=double(),
MLP_5_ASE=double(),
MLP_6_ASE=double(),
MLP_7_ASE=double(),
MLP_8_ASE=double(),
MLP_9_ASE=double(),
MLP_10_ASE=double(),
MLP_11_ASE=double(),
MLP_12_ASE=double(),
MLP_F1=double(),
MLP_F2=double(),
MLP_F3=double(),
MLP_F4=double(),
MLP_F5=double(),
MLP_F6=double(),
MLP_F7=double(),
MLP_F8=double(),
MLP_F9=double(),
MLP_F10=double(),
MLP_F11=double(),
MLP_F12=double(),
ACTUAL_1=double(),
ACTUAL_2=double(),
ACTUAL_3=double(),
ACTUAL_4=double(),
ACTUAL_5=double(),
ACTUAL_6=double(),
ACTUAL_7=double(),
ACTUAL_8=double(),
ACTUAL_9=double(),
ACTUAL_10=double(),
ACTUAL_11=double(),
ACTUAL_12=double(),
AR_F_Tally=double(),
AR_F_Conclusion=double(),
ARI_F_Tally=double(),
ARI_F_Conclusion=double(),
ARIS_F_Tally=double(),
ARIS_F_Conclusion=double(),
stringsAsFactors = FALSE)
# loop through sample combinations
for(i in 1:z) {
sample_combinations1 = combinations[i,]
temp1 = inner_join(temp,sample_combinations1)
product = sample_combinations1$Product
customer = sample_combinations1$Customer_ID
product_type = sample_combinations1$Customer_ID
results[i,"Product_Type"] = product_type
results[i,"Product"] = as.character(sample_combinations1$Product)
results[i,"Customer"] = customer
par(mfrow=c(1,1))
plot.ts(temp1$STD_Cases,
main=c(paste("Standard Case Sales of ", product),
paste("for Customer",customer)),
xlab="Months",
ylab="Standard Cases")
par(mfrow = c(2,2))
invisible(acf(temp1$STD_Cases, main="ACF"))
invisible(parzen.wge(temp1$STD_Cases))
invisible(acf(temp1$STD_Cases[0:length(temp1$date)/2], main="ACF for 1st Half of Series"))
invisible(acf(temp1$STD_Cases[(1+length(temp1$date)/2):length(temp1$date)], main="ACF for 2nd Half of Series"))
sink("file")
ljung_10 = ljung.wge(temp1$STD_Cases,K=10)
sink()
cat("The Ljung-Box test with K=10 has a p-value of",ljung_10$pval,".")
results[i,"ljung_10"] = ljung_10$pval
sink("file")
ljung_24 = ljung.wge(temp1$STD_Cases,K=24)
sink()
cat("The Ljung-Box test with K=24 has a p-value of",ljung_24$pval,".")
results[i,"ljung_24"] = ljung_24$pval
if (ljung_10$pval < .05 & ljung_24$pval < .05){
print("Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise.")
results[i,"ljung_results"] = "not white noise"
} else if (ljung_10$pval > .05 & ljung_24$pval < .05){
print("Ljung-Box test results: At a significance level of 0.05, the test is inconclusive.")
results[i,"ljung_results"] = "inconclusive"
} else if (ljung_10$pval < .05 & ljung_24$pval > .05){
print("Ljung-Box test results: At a significance level of 0.05, the test is inconclusive.")
results[i,"ljung_results"] = "inconclusive"
} else {
print("Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise.")
results[i,"ljung_results"] = "white noise"
}
sink("file")
aic = invisible(aic5.wge(temp1$STD_Cases,type="bic"))
sink()
for (row in 1:nrow(aic)) {
if(aic[row,1] == 0 & aic[row,2] == 0){
print("One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise.")
results[i,"top_5_bic"] = "white noise"
}
}
# Tests for stationarity
# Augmented Dickey-Fuller
adf=tseries::adf.test(temp1$STD_Cases)
results[i,"ADF"] = adf$p.value
# Kwiatkowski-Phillips-Schmidt-Shin
kpss=tseries::kpss.test(temp1$STD_Cases)
results[i,"KPSS"] = kpss$p.value
if (adf$p.value < .05 & kpss$p.value > .05){
print("Both stationarity tests indicate this time series is stationary.")
results[i,"stationarity_results"] = "stationary"
} else if (adf$p.value >= .05 & kpss$p.value <= .05){
print("Both stationarity tests indicate this time series is NOT stationary.")
results[i,"stationarity_results"] = "not stationary"
} else {
print("Both tests for stationarity were inconclusive.")
results[i,"stationarity_results"] = "inconclusive"
}
j=12
#Equal Means Model
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model0_mean = mean(temp1$STD_Cases[k:(k+(trainingSize-1))])
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - model0_mean)^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - model0_mean)^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - model0_mean)^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - model0_mean)^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - model0_mean)^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - model0_mean)^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - model0_mean)^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - model0_mean)^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - model0_mean)^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - model0_mean)^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - model0_mean)^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - model0_mean)^2)
sink()
assign(paste("EqualMeans_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - model0_mean)^2)
assign(paste("EqualMeans_DF_",k,sep=""),trainingSize-1)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("EqualMeans_1_ASE")] = WindowedASE1
results[i,paste0("EqualMeans_2_ASE")] = WindowedASE2
results[i,paste0("EqualMeans_3_ASE")] = WindowedASE3
results[i,paste0("EqualMeans_4_ASE")] = WindowedASE4
results[i,paste0("EqualMeans_5_ASE")] = WindowedASE5
results[i,paste0("EqualMeans_6_ASE")] = WindowedASE6
results[i,paste0("EqualMeans_7_ASE")] = WindowedASE7
results[i,paste0("EqualMeans_8_ASE")] = WindowedASE8
results[i,paste0("EqualMeans_9_ASE")] = WindowedASE9
results[i,paste0("EqualMeans_10_ASE")] = WindowedASE10
results[i,paste0("EqualMeans_11_ASE")] = WindowedASE11
results[i,paste0("EqualMeans_12_ASE")] = WindowedASE12
results[i,paste0("EqualMeans_F1")] = model0_mean
results[i,paste0("EqualMeans_F2")] = model0_mean
results[i,paste0("EqualMeans_F3")] = model0_mean
results[i,paste0("EqualMeans_F4")] = model0_mean
results[i,paste0("EqualMeans_F5")] = model0_mean
results[i,paste0("EqualMeans_F6")] = model0_mean
results[i,paste0("EqualMeans_F7")] = model0_mean
results[i,paste0("EqualMeans_F8")] = model0_mean
results[i,paste0("EqualMeans_F9")] = model0_mean
results[i,paste0("EqualMeans_F10")] = model0_mean
results[i,paste0("EqualMeans_F11")] = model0_mean
results[i,paste0("EqualMeans_F12")] = model0_mean
#AR Model
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],q=0,type="aic"))
if (model1$p == 0){
newphi = 1
} else {
newphi = model1$p
}
model1_est = invisible(est.ar.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],p=newphi))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = 0, s = 0, d = 0,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("AR_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
assign(paste("AR_DF_",k,sep=""),trainingSize-(newphi+1))
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("AR_1_ASE")] = WindowedASE1
results[i,paste0("AR_2_ASE")] = WindowedASE2
results[i,paste0("AR_3_ASE")] = WindowedASE3
results[i,paste0("AR_4_ASE")] = WindowedASE4
results[i,paste0("AR_5_ASE")] = WindowedASE5
results[i,paste0("AR_6_ASE")] = WindowedASE6
results[i,paste0("AR_7_ASE")] = WindowedASE7
results[i,paste0("AR_8_ASE")] = WindowedASE8
results[i,paste0("AR_9_ASE")] = WindowedASE9
results[i,paste0("AR_10_ASE")] = WindowedASE10
results[i,paste0("AR_11_ASE")] = WindowedASE11
results[i,paste0("AR_12_ASE")] = WindowedASE12
results[i,paste0("AR_F1")] = forecasts$f[1]
results[i,paste0("AR_F2")] = forecasts$f[2]
results[i,paste0("AR_F3")] = forecasts$f[3]
results[i,paste0("AR_F4")] = forecasts$f[4]
results[i,paste0("AR_F5")] = forecasts$f[5]
results[i,paste0("AR_F6")] = forecasts$f[6]
results[i,paste0("AR_F7")] = forecasts$f[7]
results[i,paste0("AR_F8")] = forecasts$f[8]
results[i,paste0("AR_F9")] = forecasts$f[9]
results[i,paste0("AR_F10")] = forecasts$f[10]
results[i,paste0("AR_F11")] = forecasts$f[11]
results[i,paste0("AR_F12")] = forecasts$f[12]
#ARMA Model
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],type="aic"))
model1_est = invisible(est.arma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],p=model1$p,q=model1$q))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = model1_est$theta, s = 0, d = 0,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARMA_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARMA_1_ASE")] = WindowedASE1
results[i,paste0("ARMA_2_ASE")] = WindowedASE2
results[i,paste0("ARMA_3_ASE")] = WindowedASE3
results[i,paste0("ARMA_4_ASE")] = WindowedASE4
results[i,paste0("ARMA_5_ASE")] = WindowedASE5
results[i,paste0("ARMA_6_ASE")] = WindowedASE6
results[i,paste0("ARMA_7_ASE")] = WindowedASE7
results[i,paste0("ARMA_8_ASE")] = WindowedASE8
results[i,paste0("ARMA_9_ASE")] = WindowedASE9
results[i,paste0("ARMA_10_ASE")] = WindowedASE10
results[i,paste0("ARMA_11_ASE")] = WindowedASE11
results[i,paste0("ARMA_12_ASE")] = WindowedASE12
results[i,paste0("ARMA_F1")] = forecasts$f[1]
results[i,paste0("ARMA_F2")] = forecasts$f[2]
results[i,paste0("ARMA_F3")] = forecasts$f[3]
results[i,paste0("ARMA_F4")] = forecasts$f[4]
results[i,paste0("ARMA_F5")] = forecasts$f[5]
results[i,paste0("ARMA_F6")] = forecasts$f[6]
results[i,paste0("ARMA_F7")] = forecasts$f[7]
results[i,paste0("ARMA_F8")] = forecasts$f[8]
results[i,paste0("ARMA_F9")] = forecasts$f[9]
results[i,paste0("ARMA_F10")] = forecasts$f[10]
results[i,paste0("ARMA_F11")] = forecasts$f[11]
results[i,paste0("ARMA_F12")] = forecasts$f[12]
#ARIMA Model with q=0 and d=1
nulldev()
temp2 = artrans.wge(temp1$STD_Cases,1)
dev.off()
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp2[k:(k+(trainingSize-1-1))],q=0,type="aic"))
model1_est = invisible(est.ar.wge(temp2[k:(k+(trainingSize-1-1))],p=model1$p))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = 0, s = 0, d = 1,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARI_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
assign(paste("ARI_DF_",k,sep=""),trainingSize-(model1$p+1))
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARI_1_ASE")] = WindowedASE1
results[i,paste0("ARI_2_ASE")] = WindowedASE2
results[i,paste0("ARI_3_ASE")] = WindowedASE3
results[i,paste0("ARI_4_ASE")] = WindowedASE4
results[i,paste0("ARI_5_ASE")] = WindowedASE5
results[i,paste0("ARI_6_ASE")] = WindowedASE6
results[i,paste0("ARI_7_ASE")] = WindowedASE7
results[i,paste0("ARI_8_ASE")] = WindowedASE8
results[i,paste0("ARI_9_ASE")] = WindowedASE9
results[i,paste0("ARI_10_ASE")] = WindowedASE10
results[i,paste0("ARI_11_ASE")] = WindowedASE11
results[i,paste0("ARI_12_ASE")] = WindowedASE12
results[i,paste0("ARI_F1")] = forecasts$f[1]
results[i,paste0("ARI_F2")] = forecasts$f[2]
results[i,paste0("ARI_F3")] = forecasts$f[3]
results[i,paste0("ARI_F4")] = forecasts$f[4]
results[i,paste0("ARI_F5")] = forecasts$f[5]
results[i,paste0("ARI_F6")] = forecasts$f[6]
results[i,paste0("ARI_F7")] = forecasts$f[7]
results[i,paste0("ARI_F8")] = forecasts$f[8]
results[i,paste0("ARI_F9")] = forecasts$f[9]
results[i,paste0("ARI_F10")] = forecasts$f[10]
results[i,paste0("ARI_F11")] = forecasts$f[11]
results[i,paste0("ARI_F12")] = forecasts$f[12]
#ARIMA Model with d=1
nulldev()
temp2 = artrans.wge(temp1$STD_Cases,1)
dev.off()
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp2[k:(k+(trainingSize-1-1))],type="aic"))
model1_est = invisible(est.arma.wge(temp2[k:(k+(trainingSize-1-1))],p=model1$p,q=model1$q))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = model1_est$theta, s = 0, d = 1,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARIMA_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARIMA_1_ASE")] = WindowedASE1
results[i,paste0("ARIMA_2_ASE")] = WindowedASE2
results[i,paste0("ARIMA_3_ASE")] = WindowedASE3
results[i,paste0("ARIMA_4_ASE")] = WindowedASE4
results[i,paste0("ARIMA_5_ASE")] = WindowedASE5
results[i,paste0("ARIMA_6_ASE")] = WindowedASE6
results[i,paste0("ARIMA_7_ASE")] = WindowedASE7
results[i,paste0("ARIMA_8_ASE")] = WindowedASE8
results[i,paste0("ARIMA_9_ASE")] = WindowedASE9
results[i,paste0("ARIMA_10_ASE")] = WindowedASE10
results[i,paste0("ARIMA_11_ASE")] = WindowedASE11
results[i,paste0("ARIMA_12_ASE")] = WindowedASE12
results[i,paste0("ARIMA_F1")] = forecasts$f[1]
results[i,paste0("ARIMA_F2")] = forecasts$f[2]
results[i,paste0("ARIMA_F3")] = forecasts$f[3]
results[i,paste0("ARIMA_F4")] = forecasts$f[4]
results[i,paste0("ARIMA_F5")] = forecasts$f[5]
results[i,paste0("ARIMA_F6")] = forecasts$f[6]
results[i,paste0("ARIMA_F7")] = forecasts$f[7]
results[i,paste0("ARIMA_F8")] = forecasts$f[8]
results[i,paste0("ARIMA_F9")] = forecasts$f[9]
results[i,paste0("ARIMA_F10")] = forecasts$f[10]
results[i,paste0("ARIMA_F11")] = forecasts$f[11]
results[i,paste0("ARIMA_F12")] = forecasts$f[12]
#ARIMA Model with q=0 and S=12
nulldev()
temp2 = artrans.wge(temp1$STD_Cases,phi.tr=c(rep(0,11),1))
dev.off()
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp2[k:(k+(trainingSize-1-12))],q=0, type="aic"))
if (model1$p == 0){
newphi = 1
} else {
newphi = model1$p
}
model1_est = invisible(est.ar.wge(temp2[k:(k+(trainingSize-1-12))],p=newphi))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = 0, s = 12, d = 0,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARIS_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
assign(paste("ARIS_DF_",k,sep=""),trainingSize-(newphi+1))
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARI_S12_1_ASE")] = WindowedASE1
results[i,paste0("ARI_S12_2_ASE")] = WindowedASE2
results[i,paste0("ARI_S12_3_ASE")] = WindowedASE3
results[i,paste0("ARI_S12_4_ASE")] = WindowedASE4
results[i,paste0("ARI_S12_5_ASE")] = WindowedASE5
results[i,paste0("ARI_S12_6_ASE")] = WindowedASE6
results[i,paste0("ARI_S12_7_ASE")] = WindowedASE7
results[i,paste0("ARI_S12_8_ASE")] = WindowedASE8
results[i,paste0("ARI_S12_9_ASE")] = WindowedASE9
results[i,paste0("ARI_S12_10_ASE")] = WindowedASE10
results[i,paste0("ARI_S12_11_ASE")] = WindowedASE11
results[i,paste0("ARI_S12_12_ASE")] = WindowedASE12
results[i,paste0("ARI_S12_F1")] = forecasts$f[1]
results[i,paste0("ARI_S12_F2")] = forecasts$f[2]
results[i,paste0("ARI_S12_F3")] = forecasts$f[3]
results[i,paste0("ARI_S12_F4")] = forecasts$f[4]
results[i,paste0("ARI_S12_F5")] = forecasts$f[5]
results[i,paste0("ARI_S12_F6")] = forecasts$f[6]
results[i,paste0("ARI_S12_F7")] = forecasts$f[7]
results[i,paste0("ARI_S12_F8")] = forecasts$f[8]
results[i,paste0("ARI_S12_F9")] = forecasts$f[9]
results[i,paste0("ARI_S12_F10")] = forecasts$f[10]
results[i,paste0("ARI_S12_F11")] = forecasts$f[11]
results[i,paste0("ARI_S12_F12")] = forecasts$f[12]
#ARIMA Model with S=12
nulldev()
temp2 = artrans.wge(temp1$STD_Cases,phi.tr=c(rep(0,11),1))
dev.off()
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp2[k:(k+(trainingSize-1-12))],type="aic"))
model1_est = invisible(est.arma.wge(temp2[k:(k+(trainingSize-1-12))],p=model1$p,q=model1$q))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = model1_est$theta, s = 12, d = 0,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARIMAS_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARIMA_S12_1_ASE")] = WindowedASE1
results[i,paste0("ARIMA_S12_2_ASE")] = WindowedASE2
results[i,paste0("ARIMA_S12_3_ASE")] = WindowedASE3
results[i,paste0("ARIMA_S12_4_ASE")] = WindowedASE4
results[i,paste0("ARIMA_S12_5_ASE")] = WindowedASE5
results[i,paste0("ARIMA_S12_6_ASE")] = WindowedASE6
results[i,paste0("ARIMA_S12_7_ASE")] = WindowedASE7
results[i,paste0("ARIMA_S12_8_ASE")] = WindowedASE8
results[i,paste0("ARIMA_S12_9_ASE")] = WindowedASE9
results[i,paste0("ARIMA_S12_10_ASE")] = WindowedASE10
results[i,paste0("ARIMA_S12_11_ASE")] = WindowedASE11
results[i,paste0("ARIMA_S12_12_ASE")] = WindowedASE12
results[i,paste0("ARIMA_S12_F1")] = forecasts$f[1]
results[i,paste0("ARIMA_S12_F2")] = forecasts$f[2]
results[i,paste0("ARIMA_S12_F3")] = forecasts$f[3]
results[i,paste0("ARIMA_S12_F4")] = forecasts$f[4]
results[i,paste0("ARIMA_S12_F5")] = forecasts$f[5]
results[i,paste0("ARIMA_S12_F6")] = forecasts$f[6]
results[i,paste0("ARIMA_S12_F7")] = forecasts$f[7]
results[i,paste0("ARIMA_S12_F8")] = forecasts$f[8]
results[i,paste0("ARIMA_S12_F9")] = forecasts$f[9]
results[i,paste0("ARIMA_S12_F10")] = forecasts$f[10]
results[i,paste0("ARIMA_S12_F11")] = forecasts$f[11]
results[i,paste0("ARIMA_S12_F12")] = forecasts$f[12]
#Random Forest
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
forecasts <- rf_ts(j, temp1[k:(k+(trainingSize-1)),], FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$forecast[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$forecast[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$forecast[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$forecast[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$forecast[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$forecast[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$forecast[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$forecast[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$forecast[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$forecast[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$forecast[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$forecast[1:12])^2)
sink()
assign(paste("RF_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$forecast)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("RF_1_ASE")] = WindowedASE1
results[i,paste0("RF_2_ASE")] = WindowedASE2
results[i,paste0("RF_3_ASE")] = WindowedASE3
results[i,paste0("RF_4_ASE")] = WindowedASE4
results[i,paste0("RF_5_ASE")] = WindowedASE5
results[i,paste0("RF_6_ASE")] = WindowedASE6
results[i,paste0("RF_7_ASE")] = WindowedASE7
results[i,paste0("RF_8_ASE")] = WindowedASE8
results[i,paste0("RF_9_ASE")] = WindowedASE9
results[i,paste0("RF_10_ASE")] = WindowedASE10
results[i,paste0("RF_11_ASE")] = WindowedASE11
results[i,paste0("RF_12_ASE")] = WindowedASE12
results[i,paste0("RF_F1")] = forecasts$forecast[1]
results[i,paste0("RF_F2")] = forecasts$forecast[2]
results[i,paste0("RF_F3")] = forecasts$forecast[3]
results[i,paste0("RF_F4")] = forecasts$forecast[4]
results[i,paste0("RF_F5")] = forecasts$forecast[5]
results[i,paste0("RF_F6")] = forecasts$forecast[6]
results[i,paste0("RF_F7")] = forecasts$forecast[7]
results[i,paste0("RF_F8")] = forecasts$forecast[8]
results[i,paste0("RF_F9")] = forecasts$forecast[9]
results[i,paste0("RF_F10")] = forecasts$forecast[10]
results[i,paste0("RF_F11")] = forecasts$forecast[11]
results[i,paste0("RF_F12")] = forecasts$forecast[12]
# MLP
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
forecasts <- nnc(temp1$STD_Cases[k:(k+(trainingSize-1))], j, 10, c(5, 10, 15, 5), FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$forecast[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$forecast[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$forecast[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$forecast[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$forecast[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$forecast[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$forecast[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$forecast[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$forecast[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$forecast[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$forecast[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$forecast[1:12])^2)
sink()
assign(paste("MLP_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$forecast)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("MLP_1_ASE")] = WindowedASE1
results[i,paste0("MLP_2_ASE")] = WindowedASE2
results[i,paste0("MLP_3_ASE")] = WindowedASE3
results[i,paste0("MLP_4_ASE")] = WindowedASE4
results[i,paste0("MLP_5_ASE")] = WindowedASE5
results[i,paste0("MLP_6_ASE")] = WindowedASE6
results[i,paste0("MLP_7_ASE")] = WindowedASE7
results[i,paste0("MLP_8_ASE")] = WindowedASE8
results[i,paste0("MLP_9_ASE")] = WindowedASE9
results[i,paste0("MLP_10_ASE")] = WindowedASE10
results[i,paste0("MLP_11_ASE")] = WindowedASE11
results[i,paste0("MLP_12_ASE")] = WindowedASE12
results[i,paste0("MLP_F1")] = forecasts$forecast[1]
results[i,paste0("MLP_F2")] = forecasts$forecast[2]
results[i,paste0("MLP_F3")] = forecasts$forecast[3]
results[i,paste0("MLP_F4")] = forecasts$forecast[4]
results[i,paste0("MLP_F5")] = forecasts$forecast[5]
results[i,paste0("MLP_F6")] = forecasts$forecast[6]
results[i,paste0("MLP_F7")] = forecasts$forecast[7]
results[i,paste0("MLP_F8")] = forecasts$forecast[8]
results[i,paste0("MLP_F9")] = forecasts$forecast[9]
results[i,paste0("MLP_F10")] = forecasts$forecast[10]
results[i,paste0("MLP_F11")] = forecasts$forecast[11]
results[i,paste0("MLP_F12")] = forecasts$forecast[12]
results[i,paste0("ACTUAL_1")] = temp1$STD_Cases[73]
results[i,paste0("ACTUAL_2")] = temp1$STD_Cases[74]
results[i,paste0("ACTUAL_3")] = temp1$STD_Cases[75]
results[i,paste0("ACTUAL_4")] = temp1$STD_Cases[76]
results[i,paste0("ACTUAL_5")] = temp1$STD_Cases[77]
results[i,paste0("ACTUAL_6")] = temp1$STD_Cases[78]
results[i,paste0("ACTUAL_7")] = temp1$STD_Cases[79]
results[i,paste0("ACTUAL_8")] = temp1$STD_Cases[80]
results[i,paste0("ACTUAL_9")] = temp1$STD_Cases[81]
results[i,paste0("ACTUAL_10")] = temp1$STD_Cases[82]
results[i,paste0("ACTUAL_11")] = temp1$STD_Cases[83]
results[i,paste0("ACTUAL_12")] = temp1$STD_Cases[84]
#graph ASEs for each Model
EqualMeans_Results <- rbind(EqualMeans_Results_1,EqualMeans_Results_2,EqualMeans_Results_3,EqualMeans_Results_4,EqualMeans_Results_5,EqualMeans_Results_6,
EqualMeans_Results_7,EqualMeans_Results_8,EqualMeans_Results_9,EqualMeans_Results_10,EqualMeans_Results_11,EqualMeans_Results_12,
EqualMeans_Results_13)
AR_Results <- rbind(AR_Results_1,AR_Results_2,AR_Results_3,AR_Results_4,AR_Results_5,AR_Results_6,AR_Results_7,AR_Results_8,AR_Results_9,AR_Results_10,
AR_Results_11,AR_Results_12,AR_Results_13)
ARMA_Results <- rbind(ARMA_Results_1,ARMA_Results_2,ARMA_Results_3,ARMA_Results_4,ARMA_Results_5,ARMA_Results_6,ARMA_Results_7,ARMA_Results_8,
ARMA_Results_9,ARMA_Results_10,ARMA_Results_11,ARMA_Results_12,ARMA_Results_13)
ARI_Results <- rbind(ARI_Results_1,ARI_Results_2,ARI_Results_3,ARI_Results_4,ARI_Results_5,ARI_Results_6,ARI_Results_7,ARI_Results_8,
ARI_Results_9,ARI_Results_10,ARI_Results_11,ARI_Results_12,ARI_Results_13)
ARIMA_Results <- rbind(ARIMA_Results_1,ARIMA_Results_2,ARIMA_Results_3,ARIMA_Results_4,ARIMA_Results_5,ARIMA_Results_6,ARIMA_Results_7,ARIMA_Results_8,
ARIMA_Results_9,ARIMA_Results_10,ARIMA_Results_11,ARIMA_Results_12,ARIMA_Results_13)
ARIS_Results <- rbind(ARIS_Results_1,ARIS_Results_2,ARIS_Results_3,ARIS_Results_4,ARIS_Results_5,ARIS_Results_6,ARIS_Results_7,ARIS_Results_8,
ARIS_Results_9,ARIS_Results_10,ARIS_Results_11,ARIS_Results_12,ARIS_Results_13)
ARIMAS_Results <- rbind(ARIMAS_Results_1,ARIMAS_Results_2,ARIMAS_Results_3,ARIMAS_Results_4,ARIMAS_Results_5,ARIMAS_Results_6,ARIMAS_Results_7,ARIMAS_Results_8,
ARIMAS_Results_9,ARIMAS_Results_10,ARIMAS_Results_11,ARIMAS_Results_12,ARIMAS_Results_13)
RF_Results <- rbind(RF_Results_1,RF_Results_2,RF_Results_3,RF_Results_4,RF_Results_5,RF_Results_6,RF_Results_7,RF_Results_8,
RF_Results_9,RF_Results_10,RF_Results_11,RF_Results_12,RF_Results_13)
MLP_Results <- rbind(MLP_Results_1,MLP_Results_2,MLP_Results_3,MLP_Results_4,MLP_Results_5,MLP_Results_6,MLP_Results_7,MLP_Results_8,
MLP_Results_9,MLP_Results_10,MLP_Results_11,MLP_Results_12,MLP_Results_13)
EqualMeans_Means <- colMeans(EqualMeans_Results)
AR_Means <- colMeans(AR_Results)
ARMA_Means <- colMeans(ARMA_Results)
ARI_Means <- colMeans(ARI_Results)
ARIMA_Means <- colMeans(ARIMA_Results)
ARIS_Means <- colMeans(ARIS_Results)
ARIMAS_Means <- colMeans(ARIMAS_Results)
RF_Means <- colMeans(RF_Results)
MLP_Means <- colMeans(MLP_Results)
Combined_Means <- data.frame(EqualMeans_Means,AR_Means, ARMA_Means, ARI_Means, ARIMA_Means, ARIS_Means, ARIMAS_Means,RF_Means,MLP_Means)
Combined_Means$horizon <- as.numeric(row.names(Combined_Means))
# more colors #73EBAE
g <- ggplot(data=Combined_Means, aes(horizon)) +
geom_line(aes(y=EqualMeans_Means, color="Equal Means"),size=1.5) +
geom_line(aes(y=AR_Means, color="AR"),size=1.5) +
geom_line(aes(y=ARMA_Means, color="ARMA"),size=1.5) +
geom_line(aes(y=ARI_Means, color="AR with d=1"),size=1.5) +
geom_line(aes(y=ARIMA_Means, color="ARIMA with d=1"),size=1.5) +
geom_line(aes(y=ARIS_Means, color="AR with s=12"),size=1.5) +
geom_line(aes(y=ARIMAS_Means, color="ARIMA with d=0, s=12"),size=1.5) +
geom_line(aes(y=RF_Means, color="Random Forest"),size=1.5) +
geom_line(aes(y=MLP_Means, color="MLP"),size=1.5) +
scale_color_manual(values = c(
'Equal Means' = '#004159',
'AR' = '#65A8C4',
'ARMA' = '#8C65D3',
'AR with d=1' = '#9A93EC',
'ARIMA with d=1' = '#0052A5',
'AR with s=12' = '#413BF7',
'ARIMA with d=0, s=12' = '#00ADCE',
'Random Forest' = '#59DBF1',
'MLP' = '#00C590'
)) +
labs(color='Models') +
scale_x_continuous(breaks=seq(0,13,1)) +
ggtitle(paste("Model ASEs for ", product,"and Customer",customer)) +
xlab("Month Ahead Forecast") +
ylab("ASE") +
theme(panel.background = element_blank(), axis.line = element_line(colour = "black"), legend.title = element_blank())
print(g)
# f-statistic calculations
EqualMeans_DF <- rbind(EqualMeans_DF_1,EqualMeans_DF_2,EqualMeans_DF_3,EqualMeans_DF_4,EqualMeans_DF_5,EqualMeans_DF_6,EqualMeans_DF_7,
EqualMeans_DF_8,EqualMeans_DF_9,EqualMeans_DF_10,EqualMeans_DF_11,EqualMeans_DF_12,EqualMeans_DF_13)
AR_DF <- rbind(AR_DF_1,AR_DF_2,AR_DF_3,AR_DF_4,AR_DF_5,AR_DF_6,AR_DF_7,AR_DF_8,AR_DF_9,AR_DF_10,AR_DF_11,AR_DF_12,AR_DF_13)
ARI_DF <- rbind(ARI_DF_1,ARI_DF_2,ARI_DF_3,ARI_DF_4,ARI_DF_5,ARI_DF_6,ARI_DF_7,ARI_DF_8,ARI_DF_9,ARI_DF_10,ARI_DF_11,ARI_DF_12,ARI_DF_13)
ARIS_DF <- rbind(ARIS_DF_1,ARIS_DF_2,ARIS_DF_3,ARIS_DF_4,ARIS_DF_5,ARIS_DF_6,ARIS_DF_7,ARIS_DF_8,ARIS_DF_9,ARIS_DF_10,ARIS_DF_11,ARIS_DF_12,ARIS_DF_13)
EqualMeans_Results <- rbind(sum(EqualMeans_Results_1),sum(EqualMeans_Results_2),sum(EqualMeans_Results_3),sum(EqualMeans_Results_4),sum(EqualMeans_Results_5),
sum(EqualMeans_Results_6),sum(EqualMeans_Results_7),sum(EqualMeans_Results_8),sum(EqualMeans_Results_9),sum(EqualMeans_Results_10),
sum(EqualMeans_Results_11),sum(EqualMeans_Results_12),sum(EqualMeans_Results_13))
AR_Results <- rbind(sum(AR_Results_1),sum(AR_Results_2),sum(AR_Results_3),sum(AR_Results_4),sum(AR_Results_5),sum(AR_Results_6),sum(AR_Results_7),
sum(AR_Results_8),sum(AR_Results_9),sum(AR_Results_10),sum(AR_Results_11),sum(AR_Results_12),sum(AR_Results_13))
ARI_Results <- rbind(sum(ARI_Results_1),sum(ARI_Results_2),sum(ARI_Results_3),sum(ARI_Results_4),sum(ARI_Results_5),sum(ARI_Results_6),sum(ARI_Results_7),
sum(ARI_Results_8),sum(ARI_Results_9),sum(ARI_Results_10),sum(ARI_Results_11),sum(ARI_Results_12),sum(ARI_Results_13))
ARIS_Results <- rbind(sum(ARIS_Results_1),sum(ARIS_Results_2),sum(ARIS_Results_3),sum(ARIS_Results_4),sum(ARIS_Results_5),sum(ARIS_Results_6),sum(ARIS_Results_7),
sum(ARIS_Results_8),sum(ARIS_Results_9),sum(ARIS_Results_10),sum(ARIS_Results_11),sum(ARIS_Results_12),sum(ARIS_Results_13))
df_model = EqualMeans_DF - AR_DF
ss_model = EqualMeans_Results - AR_Results
ms_model = ss_model/df_model
ms_ar = AR_Results/AR_DF
F = ms_model/ms_ar
AR_p_value = pf(F,df_model,AR_DF,lower.tail=FALSE)
AR_p_tally = sum(AR_p_value[,1]<.05, na.rm=TRUE)
results[i,"AR_F_Tally"] = AR_p_tally
if (AR_p_tally >= 9){
results[i,"AR_F_Conclusion"] = "Different"
} else if (AR_p_tally <= 4){
results[i,"AR_F_Conclusion"] = "Same"
} else {
results[i,"AR_F_Conclusion"] = "Inconclusive"
}
df_model = EqualMeans_DF - ARI_DF
ss_model = EqualMeans_Results - ARI_Results
ms_model = ss_model/df_model
ms_ari = ARI_Results/ARI_DF
F = ms_model/ms_ari
ARI_p_value = pf(F,df_model,ARI_DF,lower.tail=FALSE)
ARI_p_tally = sum(ARI_p_value[,1]<.05, na.rm=TRUE)
results[i,"ARI_F_Tally"] = ARI_p_tally
if (ARI_p_tally >= 9){
results[i,"ARI_F_Conclusion"] = "Different"
} else if (ARI_p_tally <= 4){
results[i,"ARI_F_Conclusion"] = "Same"
} else {
results[i,"ARI_F_Conclusion"] = "Inconclusive"
}
df_model = EqualMeans_DF - ARIS_DF
ss_model = EqualMeans_Results - ARIS_Results
ms_model = ss_model/df_model
ms_aris = ARIS_Results/ARIS_DF
F = ms_model/ms_aris
ARIS_p_value = pf(F,df_model,ARIS_DF,lower.tail=FALSE)
ARIS_p_tally = sum(ARIS_p_value[,1]<.05, na.rm=TRUE)
results[i,"ARIS_F_Tally"] = ARIS_p_tally
if (ARIS_p_tally >= 9){
results[i,"ARIS_F_Conclusion"] = "Different"
} else if (ARIS_p_tally <= 4){
results[i,"ARIS_F_Conclusion"] = "Same"
} else {
results[i,"ARIS_F_Conclusion"] = "Inconclusive"
}
}

The Ljung-Box test with K=10 has a p-value of 0.002322024 .The Ljung-Box test with K=24 has a p-value of 0.0034262 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.007674533 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is stationary."



The Ljung-Box test with K=10 has a p-value of 1.18705e-12 .The Ljung-Box test with K=24 has a p-value of 1.573741e-12 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0.006130321 .The Ljung-Box test with K=24 has a p-value of 0.03101856 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.2367016 .The Ljung-Box test with K=24 has a p-value of 0.122692 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is stationary."



The Ljung-Box test with K=10 has a p-value of 9.303002e-10 .The Ljung-Box test with K=24 has a p-value of 1.985955e-07 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0.004701657 .The Ljung-Box test with K=24 has a p-value of 0.232411 .[1] "Ljung-Box test results: At a significance level of 0.05, the test is inconclusive."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0.02343884 .The Ljung-Box test with K=24 has a p-value of 0.1671774 .[1] "Ljung-Box test results: At a significance level of 0.05, the test is inconclusive."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.7619964 .The Ljung-Box test with K=24 has a p-value of 0.5395173 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 3.036074e-07 .The Ljung-Box test with K=24 has a p-value of 1.098736e-05 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.1572929 .The Ljung-Box test with K=24 has a p-value of 0.3914269 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0.3857241 .The Ljung-Box test with K=24 has a p-value of 0.7674317 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 2.798171e-05 .The Ljung-Box test with K=24 has a p-value of 0.0008768368 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 3.111778e-09 .The Ljung-Box test with K=24 has a p-value of 1.111885e-09 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 6.819545e-12 .The Ljung-Box test with K=24 has a p-value of 7.58651e-10 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 9.048454e-08 .The Ljung-Box test with K=24 has a p-value of 5.283449e-07 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0.0009021729 .The Ljung-Box test with K=24 has a p-value of 0.01759268 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is stationary."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 1.110223e-16 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0.0739774 .The Ljung-Box test with K=24 has a p-value of 0.1020833 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is stationary."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.1224362 .The Ljung-Box test with K=24 has a p-value of 0.4792812 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.1225766 .The Ljung-Box test with K=24 has a p-value of 0.5601297 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."


results$winning_1 <- colnames(results[c("EqualMeans_1_ASE","AR_1_ASE","ARMA_1_ASE","ARI_1_ASE","ARIMA_1_ASE","ARI_S12_1_ASE","ARIMA_S12_1_ASE","RF_1_ASE","MLP_1_ASE")])[apply(results[c("EqualMeans_1_ASE","AR_1_ASE","ARMA_1_ASE","ARI_1_ASE","ARIMA_1_ASE","ARI_S12_1_ASE","ARIMA_S12_1_ASE","RF_1_ASE","MLP_1_ASE")],1,which.min)]
results$winning_2 <- colnames(results[c("EqualMeans_2_ASE","AR_2_ASE","ARMA_2_ASE","ARI_2_ASE","ARIMA_2_ASE","ARI_S12_2_ASE","ARIMA_S12_2_ASE","RF_2_ASE","MLP_2_ASE")])[apply(results[c("EqualMeans_2_ASE","AR_2_ASE","ARMA_2_ASE","ARI_2_ASE","ARIMA_2_ASE","ARI_S12_2_ASE","ARIMA_S12_2_ASE","RF_2_ASE","MLP_2_ASE")],1,which.min)]
results$winning_3 <- colnames(results[c("EqualMeans_3_ASE","AR_3_ASE","ARMA_3_ASE","ARI_3_ASE","ARIMA_3_ASE","ARI_S12_3_ASE","ARIMA_S12_3_ASE","RF_3_ASE","MLP_3_ASE")])[apply(results[c("EqualMeans_3_ASE","AR_3_ASE","ARMA_3_ASE","ARI_3_ASE","ARIMA_3_ASE","ARI_S12_3_ASE","ARIMA_S12_3_ASE","RF_3_ASE","MLP_3_ASE")],1,which.min)]
results$winning_4 <- colnames(results[c("EqualMeans_4_ASE","AR_4_ASE","ARMA_4_ASE","ARI_4_ASE","ARIMA_4_ASE","ARI_S12_4_ASE","ARIMA_S12_4_ASE","RF_4_ASE","MLP_4_ASE")])[apply(results[c("EqualMeans_4_ASE","AR_4_ASE","ARMA_4_ASE","ARI_4_ASE","ARIMA_4_ASE","ARI_S12_4_ASE","ARIMA_S12_4_ASE","RF_4_ASE","MLP_4_ASE")],1,which.min)]
results$winning_5 <- colnames(results[c("EqualMeans_5_ASE","AR_5_ASE","ARMA_5_ASE","ARI_5_ASE","ARIMA_5_ASE","ARI_S12_5_ASE","ARIMA_S12_5_ASE","RF_5_ASE","MLP_5_ASE")])[apply(results[c("EqualMeans_5_ASE","AR_5_ASE","ARMA_5_ASE","ARI_5_ASE","ARIMA_5_ASE","ARI_S12_5_ASE","ARIMA_S12_5_ASE","RF_5_ASE","MLP_5_ASE")],1,which.min)]
results$winning_6 <- colnames(results[c("EqualMeans_6_ASE","AR_6_ASE","ARMA_6_ASE","ARI_6_ASE","ARIMA_6_ASE","ARI_S12_6_ASE","ARIMA_S12_6_ASE","RF_6_ASE","MLP_6_ASE")])[apply(results[c("EqualMeans_6_ASE","AR_6_ASE","ARMA_6_ASE","ARI_6_ASE","ARIMA_6_ASE","ARI_S12_6_ASE","ARIMA_S12_6_ASE","RF_6_ASE","MLP_6_ASE")],1,which.min)]
results$winning_7 <- colnames(results[c("EqualMeans_7_ASE","AR_7_ASE","ARMA_7_ASE","ARI_7_ASE","ARIMA_7_ASE","ARI_S12_7_ASE","ARIMA_S12_7_ASE","RF_7_ASE","MLP_7_ASE")])[apply(results[c("EqualMeans_7_ASE","AR_7_ASE","ARMA_7_ASE","ARI_7_ASE","ARIMA_7_ASE","ARI_S12_7_ASE","ARIMA_S12_7_ASE","RF_7_ASE","MLP_7_ASE")],1,which.min)]
results$winning_8 <- colnames(results[c("EqualMeans_8_ASE","AR_8_ASE","ARMA_8_ASE","ARI_8_ASE","ARIMA_8_ASE","ARI_S12_8_ASE","ARIMA_S12_8_ASE","RF_8_ASE","MLP_8_ASE")])[apply(results[c("EqualMeans_8_ASE","AR_8_ASE","ARMA_8_ASE","ARI_8_ASE","ARIMA_8_ASE","ARI_S12_8_ASE","ARIMA_S12_8_ASE","RF_8_ASE","MLP_8_ASE")],1,which.min)]
results$winning_9 <- colnames(results[c("EqualMeans_9_ASE","AR_9_ASE","ARMA_9_ASE","ARI_9_ASE","ARIMA_9_ASE","ARI_S12_9_ASE","ARIMA_S12_9_ASE","RF_9_ASE","MLP_9_ASE")])[apply(results[c("EqualMeans_9_ASE","AR_9_ASE","ARMA_9_ASE","ARI_9_ASE","ARIMA_9_ASE","ARI_S12_9_ASE","ARIMA_S12_9_ASE","RF_9_ASE","MLP_9_ASE")],1,which.min)]
results$winning_10 <- colnames(results[c("EqualMeans_10_ASE","AR_10_ASE","ARMA_10_ASE","ARI_10_ASE","ARIMA_10_ASE","ARI_S12_10_ASE","ARIMA_S12_10_ASE","RF_10_ASE","MLP_10_ASE")])[apply(results[c("EqualMeans_10_ASE","AR_10_ASE","ARMA_10_ASE","ARI_10_ASE","ARIMA_10_ASE","ARI_S12_10_ASE","ARIMA_S12_10_ASE","RF_10_ASE","MLP_10_ASE")],1,which.min)]
results$winning_11 <- colnames(results[c("EqualMeans_11_ASE","AR_11_ASE","ARMA_11_ASE","ARI_11_ASE","ARIMA_11_ASE","ARI_S12_11_ASE","ARIMA_S12_11_ASE","RF_11_ASE","MLP_11_ASE")])[apply(results[c("EqualMeans_11_ASE","AR_11_ASE","ARMA_11_ASE","ARI_11_ASE","ARIMA_11_ASE","ARI_S12_11_ASE","ARIMA_S12_11_ASE","RF_11_ASE","MLP_11_ASE")],1,which.min)]
results$winning_12 <- colnames(results[c("EqualMeans_12_ASE","AR_12_ASE","ARMA_12_ASE","ARI_12_ASE","ARIMA_12_ASE","ARI_S12_12_ASE","ARIMA_S12_12_ASE","RF_12_ASE","MLP_12_ASE")])[apply(results[c("EqualMeans_12_ASE","AR_12_ASE","ARMA_12_ASE","ARI_12_ASE","ARIMA_12_ASE","ARI_S12_12_ASE","ARIMA_S12_12_ASE","RF_12_ASE","MLP_12_ASE")],1,which.min)]
formattable(results, align = c("l", rep("r", NCOL(table_a) - 1)))
|
Product_Type
|
Product
|
Customer
|
ljung_10
|
ljung_24
|
ljung_results
|
top_5_bic
|
ADF
|
KPSS
|
stationarity_results
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
EqualMeans_F1
|
EqualMeans_F2
|
EqualMeans_F3
|
EqualMeans_F4
|
EqualMeans_F5
|
EqualMeans_F6
|
EqualMeans_F7
|
EqualMeans_F8
|
EqualMeans_F9
|
EqualMeans_F10
|
EqualMeans_F11
|
EqualMeans_F12
|
AR_1_ASE
|
AR_2_ASE
|
AR_3_ASE
|
AR_4_ASE
|
AR_5_ASE
|
AR_6_ASE
|
AR_7_ASE
|
AR_8_ASE
|
AR_9_ASE
|
AR_10_ASE
|
AR_11_ASE
|
AR_12_ASE
|
AR_F1
|
AR_F2
|
AR_F3
|
AR_F4
|
AR_F5
|
AR_F6
|
AR_F7
|
AR_F8
|
AR_F9
|
AR_F10
|
AR_F11
|
AR_F12
|
ARMA_1_ASE
|
ARMA_2_ASE
|
ARMA_3_ASE
|
ARMA_4_ASE
|
ARMA_5_ASE
|
ARMA_6_ASE
|
ARMA_7_ASE
|
ARMA_8_ASE
|
ARMA_9_ASE
|
ARMA_10_ASE
|
ARMA_11_ASE
|
ARMA_12_ASE
|
ARMA_F1
|
ARMA_F2
|
ARMA_F3
|
ARMA_F4
|
ARMA_F5
|
ARMA_F6
|
ARMA_F7
|
ARMA_F8
|
ARMA_F9
|
ARMA_F10
|
ARMA_F11
|
ARMA_F12
|
ARI_1_ASE
|
ARI_2_ASE
|
ARI_3_ASE
|
ARI_4_ASE
|
ARI_5_ASE
|
ARI_6_ASE
|
ARI_7_ASE
|
ARI_8_ASE
|
ARI_9_ASE
|
ARI_10_ASE
|
ARI_11_ASE
|
ARI_12_ASE
|
ARI_F1
|
ARI_F2
|
ARI_F3
|
ARI_F4
|
ARI_F5
|
ARI_F6
|
ARI_F7
|
ARI_F8
|
ARI_F9
|
ARI_F10
|
ARI_F11
|
ARI_F12
|
ARIMA_1_ASE
|
ARIMA_2_ASE
|
ARIMA_3_ASE
|
ARIMA_4_ASE
|
ARIMA_5_ASE
|
ARIMA_6_ASE
|
ARIMA_7_ASE
|
ARIMA_8_ASE
|
ARIMA_9_ASE
|
ARIMA_10_ASE
|
ARIMA_11_ASE
|
ARIMA_12_ASE
|
ARIMA_F1
|
ARIMA_F2
|
ARIMA_F3
|
ARIMA_F4
|
ARIMA_F5
|
ARIMA_F6
|
ARIMA_F7
|
ARIMA_F8
|
ARIMA_F9
|
ARIMA_F10
|
ARIMA_F11
|
ARIMA_F12
|
ARI_S12_1_ASE
|
ARI_S12_2_ASE
|
ARI_S12_3_ASE
|
ARI_S12_4_ASE
|
ARI_S12_5_ASE
|
ARI_S12_6_ASE
|
ARI_S12_7_ASE
|
ARI_S12_8_ASE
|
ARI_S12_9_ASE
|
ARI_S12_10_ASE
|
ARI_S12_11_ASE
|
ARI_S12_12_ASE
|
ARI_S12_F1
|
ARI_S12_F2
|
ARI_S12_F3
|
ARI_S12_F4
|
ARI_S12_F5
|
ARI_S12_F6
|
ARI_S12_F7
|
ARI_S12_F8
|
ARI_S12_F9
|
ARI_S12_F10
|
ARI_S12_F11
|
ARI_S12_F12
|
ARIMA_S12_1_ASE
|
ARIMA_S12_2_ASE
|
ARIMA_S12_3_ASE
|
ARIMA_S12_4_ASE
|
ARIMA_S12_5_ASE
|
ARIMA_S12_6_ASE
|
ARIMA_S12_7_ASE
|
ARIMA_S12_8_ASE
|
ARIMA_S12_9_ASE
|
ARIMA_S12_10_ASE
|
ARIMA_S12_11_ASE
|
ARIMA_S12_12_ASE
|
ARIMA_S12_F1
|
ARIMA_S12_F2
|
ARIMA_S12_F3
|
ARIMA_S12_F4
|
ARIMA_S12_F5
|
ARIMA_S12_F6
|
ARIMA_S12_F7
|
ARIMA_S12_F8
|
ARIMA_S12_F9
|
ARIMA_S12_F10
|
ARIMA_S12_F11
|
ARIMA_S12_F12
|
RF_1_ASE
|
RF_2_ASE
|
RF_3_ASE
|
RF_4_ASE
|
RF_5_ASE
|
RF_6_ASE
|
RF_7_ASE
|
RF_8_ASE
|
RF_9_ASE
|
RF_10_ASE
|
RF_11_ASE
|
RF_12_ASE
|
RF_F1
|
RF_F2
|
RF_F3
|
RF_F4
|
RF_F5
|
RF_F6
|
RF_F7
|
RF_F8
|
RF_F9
|
RF_F10
|
RF_F11
|
RF_F12
|
MLP_1_ASE
|
MLP_2_ASE
|
MLP_3_ASE
|
MLP_4_ASE
|
MLP_5_ASE
|
MLP_6_ASE
|
MLP_7_ASE
|
MLP_8_ASE
|
MLP_9_ASE
|
MLP_10_ASE
|
MLP_11_ASE
|
MLP_12_ASE
|
MLP_F1
|
MLP_F2
|
MLP_F3
|
MLP_F4
|
MLP_F5
|
MLP_F6
|
MLP_F7
|
MLP_F8
|
MLP_F9
|
MLP_F10
|
MLP_F11
|
MLP_F12
|
ACTUAL_1
|
ACTUAL_2
|
ACTUAL_3
|
ACTUAL_4
|
ACTUAL_5
|
ACTUAL_6
|
ACTUAL_7
|
ACTUAL_8
|
ACTUAL_9
|
ACTUAL_10
|
ACTUAL_11
|
ACTUAL_12
|
AR_F_Tally
|
AR_F_Conclusion
|
ARI_F_Tally
|
ARI_F_Conclusion
|
ARIS_F_Tally
|
ARIS_F_Conclusion
|
winning_1
|
winning_2
|
winning_3
|
winning_4
|
winning_5
|
winning_6
|
winning_7
|
winning_8
|
winning_9
|
winning_10
|
winning_11
|
winning_12
|
|
700005925
|
TAAKA VODKA 80 1L
|
700005925
|
2.322024e-03
|
3.426200e-03
|
not white noise
|
white noise
|
0.16051598
|
0.08236679
|
inconclusive
|
819.7760806
|
812.0658241
|
815.1706105
|
689.8962088
|
601.4447370
|
535.9172216
|
489.1170549
|
453.3300453
|
418.8453911
|
390.5514370
|
364.9275910
|
346.4278690
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
1177.6244193
|
978.5196647
|
902.4422639
|
751.9989173
|
645.2789428
|
571.7912574
|
518.0486590
|
477.8120609
|
439.9488641
|
409.2232404
|
381.7398073
|
361.6837778
|
56.4915307
|
52.0502457
|
52.7957777
|
52.6563411
|
51.7062837
|
51.9118351
|
51.6557065
|
51.4741287
|
51.4785061
|
51.3733314
|
51.3337370
|
51.3135431
|
1220.5831606
|
1064.5110370
|
1020.3968276
|
830.2558671
|
695.6295191
|
611.3732866
|
546.3263424
|
504.5024731
|
459.2102364
|
428.8023578
|
399.9887614
|
380.7868738
|
53.2174811
|
53.1162966
|
56.4704512
|
49.4069529
|
54.6932876
|
51.3134915
|
52.7815933
|
52.1860505
|
51.1159264
|
52.8831414
|
50.7506623
|
52.3671199
|
1214.2332055
|
1015.2921504
|
938.4920537
|
808.0033204
|
686.8916915
|
639.1105693
|
577.9161054
|
536.2514127
|
505.2446771
|
472.0050923
|
449.2638583
|
426.9739251
|
60.845915
|
49.4615997
|
62.0138866
|
52.3476761
|
55.7535902
|
57.6035076
|
53.3478106
|
58.2313454
|
53.7935218
|
56.5314612
|
56.1117596
|
54.8232972
|
1.019355e+03
|
922.8713034
|
881.9550582
|
728.1959511
|
631.0542952
|
564.5865050
|
514.4789610
|
473.0086187
|
442.0050082
|
409.5462412
|
383.0871323
|
361.6927564
|
43.2661616
|
49.3228573
|
45.0704739
|
48.2247324
|
45.8328942
|
47.6618459
|
46.2589500
|
47.3362781
|
46.5086135
|
47.1445714
|
46.6558885
|
47.0314102
|
893.8762186
|
814.9139459
|
889.4334274
|
878.0046680
|
820.6963147
|
780.1064112
|
744.4318734
|
722.5259322
|
704.2269863
|
690.0382715
|
679.6170963
|
677.0644941
|
4.271114e+01
|
56.8988570
|
132.0143853
|
1.399795e+01
|
7.590029e+01
|
5.999996e+01
|
6.800001e+01
|
78.9999992
|
41.0000001
|
69.99999998
|
54.0000000
|
5.300000e+01
|
1057.5807655
|
1102.1703032
|
1124.8257798
|
1062.8228638
|
972.4991512
|
912.1410119
|
858.1841098
|
821.6447126
|
793.4708438
|
777.0434603
|
756.2249502
|
747.7752374
|
4.200000e+01
|
57.0000000
|
132.0000000
|
1.400000e+01
|
7.590000e+01
|
6.000000e+01
|
6.800000e+01
|
79.0000000
|
41.000000
|
70.00000000
|
54.000000
|
5.300000e+01
|
677.5376902
|
672.0302239
|
667.9522967
|
581.3353826
|
488.6867852
|
421.5943371
|
374.4843922
|
336.7191703
|
305.6265898
|
283.3723851
|
263.9415954
|
251.8716028
|
44.78000667
|
57.5735733
|
66.0851667
|
40.10861333
|
66.0851667
|
59.24699333
|
64.1085600
|
66.0851667
|
44.3042533
|
64.1678600
|
55.3119600
|
54.81204333
|
1626.7719316
|
1340.8483610
|
1290.1894880
|
1070.3831591
|
953.3553657
|
867.5341194
|
802.7019114
|
753.9624096
|
706.0567608
|
656.2031659
|
611.4135140
|
580.5781610
|
32.17810845
|
41.5492581
|
43.7429295
|
43.8813073
|
43.3119445
|
43.5997174
|
44.6649832
|
38.4759884
|
45.8302834
|
41.13984728
|
49.6347994
|
48.7468377
|
60
|
52.0
|
67
|
58
|
73
|
57
|
63.0
|
64.9
|
47.0
|
50
|
48
|
29.0
|
4
|
Same
|
9
|
Different
|
3
|
Same
|
RF_1_ASE
|
RF_2_ASE
|
RF_3_ASE
|
RF_4_ASE
|
RF_5_ASE
|
RF_6_ASE
|
RF_7_ASE
|
RF_8_ASE
|
RF_9_ASE
|
RF_10_ASE
|
RF_11_ASE
|
RF_12_ASE
|
|
701000317
|
TAAKA VODKA 80 1L
|
701000317
|
7.674533e-03
|
0.000000e+00
|
not white noise
|
white noise
|
0.01000000
|
0.10000000
|
stationary
|
5386.0238675
|
5396.0610470
|
6184.2691667
|
5551.7975855
|
5173.2864316
|
4930.6700214
|
4762.6696551
|
4625.7466239
|
4532.8720157
|
4471.5482265
|
4408.0742172
|
4359.0114744
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
5035.1703107
|
5218.7360889
|
6067.1292134
|
5463.9904498
|
5103.1262789
|
4872.2197743
|
4712.5738354
|
4581.9131435
|
4493.9089720
|
4436.4814879
|
4376.1953620
|
4329.7891886
|
94.8965765
|
100.6204215
|
101.5869617
|
101.7501736
|
101.7777339
|
101.7823878
|
101.7831737
|
101.7833064
|
101.7833288
|
101.7833326
|
101.7833332
|
101.7833333
|
5075.7624303
|
5322.5093370
|
5872.8687369
|
5059.3211299
|
4602.3842109
|
4469.0630501
|
4407.4131580
|
4192.1642408
|
4009.6761962
|
3890.3554742
|
3842.3155123
|
3826.2041051
|
95.1469980
|
111.2239452
|
130.6665597
|
134.9830893
|
132.6160184
|
120.0699014
|
107.4203227
|
94.9279002
|
87.1687948
|
84.0405840
|
86.5048168
|
92.1487011
|
6607.0465449
|
7721.2289278
|
9528.0691323
|
9245.2105154
|
9221.0967129
|
9132.5798545
|
9014.7805722
|
8680.0288513
|
8219.4212424
|
7713.4636748
|
7195.1123577
|
6708.3364610
|
57.768599
|
48.5952996
|
50.8681015
|
53.8012251
|
53.8899160
|
52.1873256
|
52.2738219
|
52.8118326
|
52.9513696
|
52.6621260
|
52.6217593
|
52.7090493
|
1.067297e+04
|
8896.2364736
|
9681.2959002
|
7939.4196886
|
7844.4094248
|
7990.1810252
|
8554.5884303
|
8495.5687722
|
8648.9829702
|
8580.5019700
|
8400.6342571
|
8098.7569531
|
100.7928105
|
106.1108503
|
104.0874318
|
98.5400584
|
96.0526262
|
95.8392398
|
96.5603734
|
97.1166996
|
97.2837055
|
97.2210394
|
97.1236224
|
97.0748714
|
431.8207267
|
454.5782997
|
515.4262725
|
515.4481941
|
512.5350830
|
548.3198016
|
569.9995070
|
594.0760074
|
613.9132478
|
634.0917989
|
635.8335107
|
637.6881522
|
6.164025e+01
|
133.0261755
|
329.6865732
|
1.130957e+02
|
1.059708e+02
|
1.240089e+02
|
1.359973e+02
|
103.0008331
|
69.9997455
|
25.00007772
|
46.9999763
|
6.100001e+01
|
8433.3857923
|
5154.1237411
|
3763.1088122
|
2967.9427354
|
2477.4753201
|
2186.1939136
|
1971.8970771
|
1821.0071934
|
1703.7337292
|
1614.6880514
|
1527.2622155
|
1454.6370126
|
6.260246e+01
|
132.0000000
|
330.0000000
|
1.130000e+02
|
1.060000e+02
|
1.240000e+02
|
1.360000e+02
|
103.0000000
|
70.000000
|
25.00000000
|
47.000000
|
6.100000e+01
|
2009.2674629
|
2235.8452139
|
2347.1904314
|
2158.0062389
|
2107.9777941
|
2112.5452004
|
2147.9195318
|
2081.7304947
|
1988.3184262
|
1886.0493562
|
1794.2868689
|
1711.8133189
|
58.31030000
|
98.8225667
|
246.5644000
|
92.59386667
|
90.1950000
|
98.19896667
|
99.1769667
|
89.7599000
|
59.9459333
|
47.3314000
|
51.5643000
|
57.93950000
|
7302.8322258
|
6946.6753454
|
7817.3709866
|
6976.5648298
|
6350.9840361
|
5963.1064014
|
5749.2526523
|
5576.1605840
|
5461.4797738
|
5353.9654920
|
5242.6849756
|
5140.9396995
|
66.17634435
|
73.0283908
|
76.5870901
|
75.1246430
|
86.1783977
|
92.4855528
|
92.4855540
|
92.4855541
|
92.4855541
|
75.59315357
|
86.6490804
|
92.4855541
|
88
|
125.0
|
280
|
136
|
113
|
69
|
139.0
|
74.0
|
56.0
|
50
|
46
|
52.0
|
0
|
Same
|
0
|
Same
|
13
|
Different
|
ARI_S12_1_ASE
|
ARI_S12_2_ASE
|
ARI_S12_3_ASE
|
ARI_S12_4_ASE
|
ARI_S12_5_ASE
|
ARI_S12_6_ASE
|
ARI_S12_7_ASE
|
ARI_S12_8_ASE
|
ARI_S12_9_ASE
|
ARI_S12_10_ASE
|
ARI_S12_11_ASE
|
ARI_S12_12_ASE
|
|
701001770
|
TAAKA VODKA 80 1L
|
701001770
|
1.187050e-12
|
1.573741e-12
|
not white noise
|
NA
|
0.53188120
|
0.01000000
|
not stationary
|
6037.8142344
|
6011.6269267
|
6006.4802173
|
5164.7525036
|
4552.7806959
|
4119.4096190
|
3804.3090329
|
3566.8000677
|
3361.0108897
|
3202.4520857
|
3057.9253719
|
2878.2446917
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
6488.5970271
|
6395.9339005
|
6521.3455398
|
5842.0743353
|
4867.8635968
|
4310.0105737
|
3868.3217124
|
3529.3701005
|
3233.2800040
|
3017.4922756
|
2848.6383501
|
2662.4500660
|
122.3825927
|
141.4522638
|
156.8207240
|
146.5400699
|
132.7953512
|
134.8241282
|
140.8787658
|
136.8375946
|
131.1094967
|
130.4689701
|
131.6987452
|
130.0852146
|
6069.9661721
|
5828.9569556
|
5547.8444068
|
4610.1606213
|
3791.3806203
|
3434.4124271
|
3067.2728007
|
2758.3131160
|
2544.1935701
|
2414.8295759
|
2316.1141232
|
2178.9429165
|
126.1569793
|
146.0084226
|
143.7054915
|
138.8871720
|
139.8964381
|
139.0296243
|
137.7483457
|
137.0800734
|
136.2793895
|
135.4398483
|
134.6801564
|
133.9294398
|
6965.6153532
|
7278.5744593
|
7251.8801768
|
6406.9688800
|
5337.7932017
|
4720.5019793
|
4240.2377469
|
3853.4744897
|
3581.0515812
|
3352.6918536
|
3201.0444177
|
3043.0077283
|
130.608397
|
149.5099179
|
167.8357808
|
158.6284493
|
146.5277043
|
151.2585751
|
159.1897122
|
156.5399797
|
151.7689753
|
152.9420884
|
155.9654258
|
155.4518223
|
5.254440e+03
|
4999.1257339
|
5154.8823639
|
4783.7655494
|
4074.9633382
|
3593.7634359
|
3216.2823825
|
2939.4821732
|
2754.5828734
|
2607.4836920
|
2519.5146881
|
2417.0835426
|
130.6083974
|
149.5099179
|
167.8357808
|
158.6284493
|
146.5277043
|
151.2585751
|
159.1897122
|
156.5399797
|
151.7689753
|
152.9420884
|
155.9654258
|
155.4518223
|
6105.4555197
|
5761.0267969
|
6212.9203410
|
5915.2780651
|
5626.1279553
|
5448.5127186
|
5299.6359268
|
5191.4314014
|
5091.0703660
|
5004.9910085
|
4973.3309083
|
4973.9199318
|
1.202644e+02
|
139.3387993
|
306.7948196
|
7.031445e+00
|
1.549952e+02
|
1.270007e+02
|
1.179999e+02
|
154.0000173
|
91.9999973
|
148.00000041
|
191.9999999
|
1.620000e+02
|
6366.8174714
|
6162.0168690
|
6965.4999529
|
6622.7382835
|
6228.5606941
|
6005.9191192
|
5770.7234801
|
5585.5098089
|
5469.6374123
|
5399.0224535
|
5393.4726932
|
5417.6119722
|
1.290000e+02
|
138.0000000
|
307.0000000
|
7.000000e+00
|
1.550000e+02
|
1.270000e+02
|
1.180000e+02
|
154.0000000
|
92.000000
|
148.00000000
|
192.000000
|
1.620000e+02
|
4476.2253472
|
4485.6273912
|
4539.9970884
|
3825.5443318
|
3204.6010793
|
2861.9519095
|
2649.6991122
|
2504.8517974
|
2371.5942488
|
2255.4476137
|
2197.0476437
|
2149.1009365
|
139.29516000
|
142.0430633
|
173.5373433
|
97.21386333
|
145.1196500
|
137.66300667
|
128.4414800
|
145.1196500
|
111.5212533
|
143.8662167
|
173.5373433
|
158.10345667
|
4773.2631881
|
4787.6204258
|
4801.1928451
|
4109.9659070
|
3505.4248915
|
3088.1662382
|
2816.1203786
|
2581.6061954
|
2383.8898748
|
2241.7188533
|
2116.6974659
|
1988.3585582
|
119.62106270
|
116.9377401
|
112.1221545
|
116.6896836
|
114.9349521
|
110.6252980
|
114.5338947
|
123.7486316
|
113.9088299
|
110.22667138
|
109.1751398
|
115.3994394
|
147
|
117.0
|
142
|
133
|
111
|
83
|
95.0
|
114.0
|
91.9
|
104
|
117
|
100.0
|
4
|
Same
|
5
|
Inconclusive
|
2
|
Same
|
RF_1_ASE
|
RF_2_ASE
|
RF_3_ASE
|
RF_4_ASE
|
RF_5_ASE
|
RF_6_ASE
|
RF_7_ASE
|
RF_8_ASE
|
RF_9_ASE
|
MLP_10_ASE
|
MLP_11_ASE
|
MLP_12_ASE
|
|
701001790
|
TAAKA VODKA 80 1L
|
701001790
|
6.130321e-03
|
3.101856e-02
|
not white noise
|
NA
|
0.23862134
|
0.10000000
|
inconclusive
|
656.5598284
|
653.9191489
|
657.4544780
|
565.9303476
|
481.6666900
|
426.3206618
|
387.9143779
|
358.5476137
|
336.9679082
|
318.7005028
|
304.7056093
|
294.3995806
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
786.5930500
|
716.3372144
|
712.5096524
|
608.2918395
|
514.4203764
|
461.0356173
|
418.4398183
|
383.9446630
|
355.6715510
|
333.1692130
|
315.3258779
|
302.7288698
|
51.6172774
|
57.0747935
|
53.3426211
|
51.0867726
|
51.0975895
|
49.6858496
|
49.2853439
|
48.8737669
|
48.4875122
|
48.2957402
|
48.1040299
|
47.9758083
|
786.6821475
|
714.5797864
|
711.4097381
|
607.2602509
|
513.3486510
|
460.5636041
|
418.4143139
|
384.0363764
|
356.1047069
|
333.7082767
|
316.0337581
|
303.6017446
|
50.1138106
|
56.0235607
|
52.5582075
|
52.5577752
|
51.5403314
|
51.0307437
|
50.4769171
|
50.0500161
|
49.6736841
|
49.3599313
|
49.0923578
|
48.8662770
|
914.0287943
|
799.3570062
|
790.6682658
|
685.3500082
|
591.7141719
|
552.9409655
|
513.8854120
|
478.0154518
|
444.5616395
|
418.0618258
|
397.7465833
|
385.4888968
|
59.049900
|
65.6257614
|
64.5197884
|
63.1589165
|
64.7194735
|
63.8546948
|
64.0502566
|
64.1860772
|
64.0003658
|
64.1124673
|
64.0812833
|
64.0686986
|
8.578247e+02
|
740.6271509
|
750.6761298
|
646.1743870
|
547.8952825
|
502.9643017
|
458.7333820
|
422.7623404
|
390.8696895
|
368.0898651
|
352.1400245
|
344.3114579
|
52.6895755
|
58.3425279
|
56.6876771
|
57.1721197
|
57.0303035
|
57.0718189
|
57.0596657
|
57.0632234
|
57.0621819
|
57.0624868
|
57.0623976
|
57.0624237
|
780.8708943
|
718.2994717
|
809.5499929
|
808.6626919
|
758.7096754
|
727.8598668
|
707.9002126
|
694.1311879
|
681.0637345
|
670.4667210
|
666.5287772
|
676.1579899
|
6.789311e+01
|
55.1070490
|
119.9267300
|
6.368655e+00
|
5.489771e+01
|
5.193155e+01
|
5.440356e+01
|
55.3706734
|
35.1773121
|
54.14892463
|
59.0767145
|
7.206030e+01
|
842.6917713
|
898.1286013
|
959.1236222
|
904.9257325
|
814.4104498
|
774.4453964
|
743.4284306
|
731.9260579
|
714.1077562
|
702.2044549
|
696.7813044
|
699.6595046
|
6.789311e+01
|
55.1070490
|
119.9267300
|
6.368655e+00
|
5.489771e+01
|
5.193155e+01
|
5.440356e+01
|
55.3706734
|
35.177312
|
54.14892463
|
59.076715
|
7.206030e+01
|
578.1082136
|
575.6157280
|
570.7736948
|
516.3770639
|
435.4491186
|
384.7926711
|
351.7960350
|
326.9359670
|
308.4523974
|
292.0029125
|
283.2612056
|
281.5804943
|
58.73956667
|
50.5562667
|
60.4062333
|
33.87182000
|
54.6964667
|
52.23450000
|
54.6964667
|
55.6670667
|
37.2621967
|
54.6964667
|
57.8414000
|
60.40623333
|
596.4441001
|
536.3324039
|
555.1023137
|
484.9851129
|
407.6498666
|
355.2203243
|
322.6561843
|
298.2118689
|
280.9969834
|
267.3889590
|
256.6234448
|
252.9205554
|
56.44188977
|
50.0326353
|
53.9840618
|
39.7563976
|
46.2034752
|
53.2079140
|
44.0259732
|
48.3147209
|
50.3692325
|
50.74911637
|
48.2999796
|
51.0392556
|
63
|
64.9
|
59
|
55
|
52
|
39
|
35.0
|
44.0
|
32.0
|
40
|
33
|
28.9
|
1
|
Same
|
2
|
Same
|
0
|
Same
|
RF_1_ASE
|
MLP_2_ASE
|
MLP_3_ASE
|
MLP_4_ASE
|
MLP_5_ASE
|
MLP_6_ASE
|
MLP_7_ASE
|
MLP_8_ASE
|
MLP_9_ASE
|
MLP_10_ASE
|
MLP_11_ASE
|
MLP_12_ASE
|
|
700005448
|
TAAKA VODKA 80 1L
|
700005448
|
2.367016e-01
|
1.226920e-01
|
white noise
|
white noise
|
0.01000000
|
0.10000000
|
stationary
|
3.2606182
|
3.2721566
|
3.2434387
|
3.0333105
|
2.7925156
|
2.5995498
|
2.4645339
|
2.3359643
|
2.2364786
|
2.1533002
|
2.0775063
|
2.0241096
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
3.3781013
|
3.3095393
|
3.2432368
|
3.0395011
|
2.7916400
|
2.5992903
|
2.4654883
|
2.3346686
|
2.2343802
|
2.1494476
|
2.0740426
|
2.0212109
|
2.7164817
|
2.7486453
|
2.7483303
|
2.7483334
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
3.3155536
|
3.6951052
|
3.5728124
|
3.2893304
|
2.9938501
|
2.7727916
|
2.6125905
|
2.4612121
|
2.3474646
|
2.2538158
|
2.1690031
|
2.1081807
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
4.8010660
|
4.6320358
|
4.8689421
|
4.7621693
|
4.5819942
|
4.3871059
|
4.1607583
|
3.9119798
|
3.6244150
|
3.3878035
|
3.2129862
|
3.1616146
|
4.523339
|
5.0464211
|
5.0789813
|
4.9065999
|
5.0246405
|
4.9863733
|
4.9811519
|
4.9958593
|
4.9864811
|
4.9892344
|
4.9898611
|
4.9886176
|
4.277557e+00
|
4.4170716
|
4.2540623
|
4.2082053
|
4.0010940
|
3.7620384
|
3.5477853
|
3.2918129
|
3.0336648
|
2.8995667
|
2.7235312
|
2.6498099
|
2.0911105
|
2.3396315
|
1.6856053
|
1.7126542
|
1.6028931
|
1.6049587
|
1.5864818
|
1.5864097
|
1.5832898
|
1.5832066
|
1.5826781
|
1.5826521
|
5.1842619
|
4.7438252
|
4.5576617
|
4.1595069
|
3.7585025
|
3.5397909
|
3.4232620
|
3.3414186
|
3.2281092
|
3.1148636
|
3.0208574
|
3.1073461
|
4.394872e+00
|
3.5629699
|
5.9933855
|
-6.026074e-01
|
8.879026e-01
|
3.114198e+00
|
9.120087e-01
|
3.1909435
|
2.0235236
|
3.96196530
|
4.0555991
|
5.935030e+00
|
5.2717011
|
4.8712880
|
4.5640162
|
4.1850058
|
3.8107385
|
3.6754518
|
3.5445104
|
3.4088147
|
3.2588620
|
3.1409703
|
3.0572035
|
3.1602990
|
4.394872e+00
|
3.5629699
|
5.9933855
|
-6.026074e-01
|
8.879026e-01
|
3.114198e+00
|
9.120087e-01
|
3.1909435
|
2.023524
|
3.96196530
|
4.055599
|
5.935030e+00
|
4.0592499
|
3.9412731
|
3.8647310
|
3.4190700
|
3.0436178
|
2.7799210
|
2.6067920
|
2.4927114
|
2.4094821
|
2.3236274
|
2.2344354
|
2.1987716
|
3.77810000
|
3.7781000
|
3.8581000
|
1.70382000
|
1.7106200
|
3.25950000
|
1.7106200
|
3.2595000
|
2.2603333
|
3.7781000
|
3.7781000
|
3.85810000
|
3.1024754
|
3.1418167
|
3.1181259
|
2.9214136
|
2.6844347
|
2.4922830
|
2.3633401
|
2.2417774
|
2.1527288
|
2.0768826
|
2.0087534
|
1.9643989
|
2.86720177
|
2.7836153
|
2.7939332
|
2.7990557
|
2.7986444
|
2.8004600
|
2.7985168
|
2.8005203
|
2.7985121
|
2.80052293
|
2.7985119
|
2.8005230
|
3
|
4.0
|
3
|
2
|
3
|
2
|
1.9
|
2.0
|
2.0
|
3
|
2
|
1.0
|
0
|
Same
|
0
|
Same
|
0
|
Same
|
MLP_1_ASE
|
MLP_2_ASE
|
MLP_3_ASE
|
MLP_4_ASE
|
MLP_5_ASE
|
MLP_6_ASE
|
MLP_7_ASE
|
MLP_8_ASE
|
MLP_9_ASE
|
MLP_10_ASE
|
MLP_11_ASE
|
MLP_12_ASE
|
|
700005910
|
TAAKA VODKA 80 1L
|
700005910
|
9.303002e-10
|
1.985955e-07
|
not white noise
|
NA
|
0.41243058
|
0.01000000
|
not stationary
|
6.7172506
|
6.7250712
|
6.5282763
|
5.5460968
|
4.8189942
|
4.3354985
|
3.8306572
|
3.4450071
|
3.1375640
|
2.8975071
|
2.7124721
|
2.5533618
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
6.0858731
|
5.4317515
|
5.0313406
|
5.2157861
|
4.7147004
|
4.2545220
|
3.9106572
|
3.4979056
|
3.2110668
|
3.0507028
|
2.8893624
|
2.7371097
|
3.9108782
|
3.8589130
|
4.3650583
|
3.8193368
|
3.7658267
|
4.0203365
|
3.7464232
|
3.7050224
|
3.8320614
|
3.6942498
|
3.6657190
|
3.7285952
|
6.1013892
|
5.9203572
|
5.1397653
|
5.1470264
|
4.7179650
|
4.3067151
|
3.9056435
|
3.4903498
|
3.2598653
|
3.0606990
|
2.8924495
|
2.7913065
|
3.9108782
|
3.8589130
|
4.3650583
|
3.8193368
|
3.7658267
|
4.0203365
|
3.7464232
|
3.7050224
|
3.8320614
|
3.6942498
|
3.6657190
|
3.7285952
|
6.4180671
|
5.5822237
|
5.1910526
|
5.7611289
|
5.3900833
|
4.9376795
|
4.7755047
|
4.4630113
|
4.2442940
|
4.1784287
|
4.1535670
|
4.0361142
|
4.196913
|
4.1858766
|
4.7215529
|
4.2985980
|
4.2868719
|
4.5737411
|
4.3510523
|
4.3417089
|
4.4952930
|
4.3780807
|
4.3714635
|
4.4536672
|
6.418067e+00
|
5.5822237
|
5.1910526
|
5.7611289
|
5.3900833
|
4.9376795
|
4.7755047
|
4.4630113
|
4.2442940
|
4.1784287
|
4.1535670
|
4.0361142
|
4.1969130
|
4.1858766
|
4.7215529
|
4.2985980
|
4.2868719
|
4.5737411
|
4.3510523
|
4.3417089
|
4.4952930
|
4.3780807
|
4.3714635
|
4.4536672
|
4.9882098
|
4.6530860
|
5.2562434
|
5.5832256
|
5.6552794
|
5.8574158
|
6.0287645
|
6.1841193
|
6.3014406
|
6.3962147
|
6.5002135
|
6.5669629
|
2.474223e+00
|
5.2479551
|
9.2218859
|
-3.577969e-01
|
3.677761e+00
|
6.687046e+00
|
1.812916e+00
|
4.8541127
|
2.8693219
|
3.91063865
|
3.9330345
|
4.943742e+00
|
12.9033412
|
14.3702306
|
13.6094087
|
12.4329890
|
13.0086409
|
12.1494337
|
11.3515892
|
10.8322386
|
10.5849708
|
10.3442391
|
10.3014626
|
10.5715793
|
2.827593e+00
|
5.0647919
|
9.9032571
|
-2.211545e-01
|
3.837791e+00
|
7.005361e+00
|
1.897978e+00
|
5.0104979
|
2.971441
|
3.98968502
|
4.001390
|
4.989016e+00
|
4.5437145
|
4.5945908
|
4.6793982
|
4.0201394
|
3.5367639
|
3.3185032
|
3.0099518
|
2.8190769
|
2.6780813
|
2.5682226
|
2.5384174
|
2.4973166
|
3.99018333
|
6.0843000
|
6.2374667
|
3.44851000
|
5.4406333
|
6.23746667
|
3.5834433
|
5.9434333
|
3.9901833
|
5.4406333
|
5.4406333
|
5.94343333
|
6.4916416
|
6.7094167
|
6.6209159
|
5.6655419
|
4.9489381
|
4.4628948
|
3.9458611
|
3.5481868
|
3.2173699
|
2.9525975
|
2.7385318
|
2.5603156
|
3.36189603
|
3.1735339
|
3.1245375
|
3.1099698
|
3.1071391
|
3.1062400
|
3.1061798
|
3.1061645
|
3.1061606
|
3.10615962
|
3.1061594
|
3.1061593
|
3
|
4.0
|
3
|
3
|
3
|
3
|
3.0
|
3.0
|
2.0
|
3
|
2
|
4.0
|
3
|
Same
|
2
|
Same
|
3
|
Same
|
RF_1_ASE
|
RF_2_ASE
|
RF_3_ASE
|
RF_4_ASE
|
RF_5_ASE
|
RF_6_ASE
|
RF_7_ASE
|
RF_8_ASE
|
RF_9_ASE
|
RF_10_ASE
|
RF_11_ASE
|
RF_12_ASE
|
|
701001906
|
TAAKA VODKA 80 1L
|
701001906
|
4.701657e-03
|
2.324110e-01
|
inconclusive
|
white noise
|
0.25007229
|
0.05352414
|
inconclusive
|
65.2421581
|
65.6767735
|
65.4695085
|
59.6613889
|
55.7436966
|
53.1857479
|
51.1165171
|
49.5479274
|
47.2526994
|
45.3416453
|
42.9647688
|
40.5688675
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
85.2887355
|
77.6839390
|
73.8069104
|
69.2578116
|
64.4633563
|
59.9397663
|
57.1697840
|
55.5471763
|
53.3873148
|
51.7330153
|
49.4464658
|
46.9257281
|
14.4189452
|
13.2802750
|
13.1946172
|
12.3121250
|
11.7450798
|
11.4520227
|
10.9466142
|
10.6014555
|
10.3353892
|
10.0302385
|
9.8010743
|
9.6030328
|
85.6532674
|
78.3684495
|
78.3403061
|
75.3847626
|
70.1241569
|
67.8788493
|
64.9288995
|
62.8958034
|
60.2088286
|
57.8759763
|
55.0380537
|
51.8806305
|
14.4341932
|
14.2992145
|
14.2981344
|
13.8378853
|
13.5695530
|
13.3717741
|
13.1063023
|
12.8707520
|
12.6607333
|
12.4485251
|
12.2471629
|
12.0581166
|
74.5872838
|
70.6186401
|
66.3973333
|
59.6478478
|
53.8327383
|
51.5811565
|
48.6152222
|
48.0686022
|
47.9420718
|
49.6530111
|
50.0749767
|
49.5147431
|
16.890080
|
14.4266741
|
16.4785885
|
15.8949595
|
15.5535072
|
16.2036223
|
15.5111407
|
15.9504079
|
15.9285781
|
15.6921148
|
15.9496297
|
15.7800948
|
7.502760e+01
|
72.4998836
|
66.2607202
|
62.0471258
|
56.6378845
|
53.7960147
|
51.3101253
|
50.7398992
|
50.4118597
|
51.3105130
|
50.7607084
|
49.5530079
|
16.8900802
|
14.4266741
|
16.4785885
|
15.8949595
|
15.5535072
|
16.2036223
|
15.5111407
|
15.9504079
|
15.9285781
|
15.6921148
|
15.9496297
|
15.7800948
|
67.1468926
|
66.2038316
|
80.2528189
|
83.0318224
|
85.0611332
|
85.5190828
|
85.6981161
|
88.5574500
|
91.0191982
|
94.5276143
|
97.4637139
|
99.4802874
|
1.378831e+01
|
16.7230399
|
31.2335027
|
6.236520e+00
|
1.494705e+01
|
1.903011e+01
|
1.666967e+01
|
23.7685567
|
15.9627628
|
22.18950698
|
19.4650976
|
2.001956e+01
|
70.6317652
|
70.6193852
|
84.1328390
|
86.1232897
|
87.6434001
|
87.6274218
|
86.3545689
|
89.4743434
|
92.0583899
|
95.7837302
|
98.3255336
|
99.7407462
|
1.378831e+01
|
16.7230399
|
31.2335027
|
6.236520e+00
|
1.494705e+01
|
1.903011e+01
|
1.666967e+01
|
23.7685567
|
15.962763
|
22.18950698
|
19.465098
|
2.001956e+01
|
66.0039822
|
65.2669838
|
64.2210178
|
59.5624969
|
56.0555223
|
53.6185380
|
51.3275461
|
49.5178577
|
46.8304084
|
44.6489859
|
42.3556792
|
40.1835077
|
5.86703333
|
9.7629000
|
9.7629000
|
5.30776667
|
9.5909333
|
9.76290000
|
9.7629000
|
9.7629000
|
9.7629000
|
9.7629000
|
9.7629000
|
9.76290000
|
58.5809757
|
64.6758492
|
67.2387161
|
62.7200042
|
59.6796979
|
57.5146653
|
55.5307521
|
53.8214504
|
51.2117272
|
48.9734325
|
46.2382131
|
43.5778354
|
8.09499620
|
6.9148215
|
6.6156480
|
6.5633902
|
6.5542264
|
6.5526352
|
6.5523594
|
6.5523116
|
6.5523033
|
6.55230184
|
6.5523016
|
6.5523015
|
5
|
10.0
|
8
|
12
|
11
|
7
|
5.0
|
4.0
|
4.0
|
7
|
5
|
10.0
|
1
|
Same
|
6
|
Inconclusive
|
3
|
Same
|
MLP_1_ASE
|
MLP_2_ASE
|
RF_3_ASE
|
RF_4_ASE
|
ARI_5_ASE
|
ARI_6_ASE
|
ARI_7_ASE
|
ARI_8_ASE
|
RF_9_ASE
|
RF_10_ASE
|
RF_11_ASE
|
RF_12_ASE
|
|
701001907
|
TAAKA VODKA 80 1L
|
701001907
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.42321723
|
0.01000000
|
not stationary
|
198.1483532
|
202.6365583
|
206.4005754
|
182.9596994
|
171.3135840
|
163.8738660
|
158.2928221
|
155.6318788
|
153.1012307
|
151.9063532
|
150.0285164
|
147.7516011
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
186.3383862
|
161.2947331
|
155.6102408
|
132.9191425
|
114.5520854
|
106.2446882
|
100.4079803
|
99.3107249
|
99.4926151
|
101.4070585
|
103.0827167
|
103.2697017
|
23.2996031
|
22.7028899
|
23.4093621
|
22.3978434
|
22.0325639
|
21.8955191
|
21.5314745
|
21.3273656
|
20.9641341
|
20.7343026
|
20.5200017
|
20.2589090
|
163.0706045
|
147.6514991
|
142.0327887
|
118.6811530
|
102.0842851
|
93.9061365
|
88.8143283
|
89.1300378
|
90.6734892
|
93.6587780
|
96.6549372
|
98.3048675
|
22.8958642
|
23.2461938
|
22.9222282
|
22.5535981
|
22.4030964
|
22.1708292
|
21.9334597
|
21.7335775
|
21.5290731
|
21.3288296
|
21.1394194
|
20.9548197
|
195.9989829
|
172.8274751
|
169.5288216
|
144.5641460
|
120.7028131
|
108.7694538
|
100.0483781
|
98.6547173
|
99.9286912
|
103.1177134
|
107.1926827
|
110.0422183
|
24.715734
|
23.8774350
|
25.1937409
|
24.4058052
|
24.2982615
|
24.6635349
|
24.4493511
|
24.6040337
|
24.4647506
|
24.4934478
|
24.5652805
|
24.4945240
|
1.501983e+02
|
143.3290122
|
141.7302822
|
119.6492689
|
106.8935998
|
97.5921329
|
92.7298300
|
94.7295674
|
97.4315877
|
102.3187664
|
106.9094471
|
111.2762217
|
22.9247349
|
23.6428572
|
23.5853520
|
23.4369136
|
23.5165456
|
23.5172404
|
23.4976499
|
23.5061535
|
23.5071332
|
23.5046404
|
23.5055066
|
23.5057268
|
180.1416808
|
162.3236925
|
170.7602707
|
164.3104602
|
159.7579607
|
155.9282852
|
154.1061057
|
157.8454601
|
161.2023830
|
167.5519152
|
173.5913865
|
178.3727557
|
1.762591e+01
|
21.0435293
|
50.0030273
|
1.000211e+00
|
2.200001e+01
|
2.500000e+01
|
1.900000e+01
|
27.0000000
|
19.0000000
|
27.00000000
|
25.0000000
|
2.400000e+01
|
182.8480991
|
165.3978276
|
172.7950756
|
165.8161092
|
160.9457245
|
156.9136823
|
154.9305126
|
158.5704392
|
161.8352075
|
168.1169377
|
174.1008125
|
178.8359459
|
1.700000e+01
|
21.0000000
|
50.0000000
|
1.000000e+00
|
2.200000e+01
|
2.500000e+01
|
1.900000e+01
|
27.0000000
|
19.000000
|
27.00000000
|
25.000000
|
2.400000e+01
|
156.4368270
|
154.9930660
|
152.8209379
|
136.5148452
|
124.6543834
|
116.2945839
|
111.1249615
|
110.0743097
|
110.3108224
|
112.0529267
|
113.8085748
|
114.6928745
|
17.15971000
|
20.2365167
|
20.7316200
|
10.18493333
|
20.5046500
|
20.73162000
|
19.0378000
|
20.7316200
|
19.0378000
|
20.7316200
|
20.7316200
|
20.73162000
|
135.6399232
|
143.3332459
|
139.7855526
|
117.0650698
|
97.6246387
|
85.2372261
|
80.0630062
|
77.6480670
|
77.8008573
|
81.3899543
|
87.0057808
|
92.5205416
|
17.20569305
|
16.8698292
|
23.7587437
|
20.3939866
|
21.7593247
|
21.6009803
|
24.0957281
|
24.4798583
|
25.4518303
|
23.91105107
|
24.3612270
|
24.3227384
|
28
|
25.0
|
26
|
23
|
30
|
22
|
7.0
|
4.0
|
5.0
|
5
|
8
|
21.0
|
11
|
Different
|
9
|
Different
|
3
|
Same
|
MLP_1_ASE
|
ARIMA_2_ASE
|
MLP_3_ASE
|
MLP_4_ASE
|
MLP_5_ASE
|
MLP_6_ASE
|
MLP_7_ASE
|
MLP_8_ASE
|
MLP_9_ASE
|
MLP_10_ASE
|
MLP_11_ASE
|
MLP_12_ASE
|
|
700005900
|
TAAKA VODKA 80 1L
|
700005900
|
2.343884e-02
|
1.671774e-01
|
inconclusive
|
white noise
|
0.18041707
|
0.04977124
|
not stationary
|
1.1002137
|
1.2694444
|
1.3378205
|
1.4354701
|
1.5084188
|
1.5634615
|
1.6097375
|
1.6434829
|
1.6663105
|
1.6832906
|
1.7279526
|
1.7576923
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.1165974
|
1.3220911
|
1.4568656
|
1.5441073
|
1.6008962
|
1.6276643
|
1.6604292
|
1.6866816
|
1.7060960
|
1.7198011
|
1.7614605
|
1.7883535
|
2.1186626
|
1.9329273
|
1.8937849
|
1.8855359
|
1.8837975
|
1.8834312
|
1.8833539
|
1.8833377
|
1.8833342
|
1.8833335
|
1.8833334
|
1.8833333
|
1.0595516
|
1.3538109
|
1.4839001
|
1.5426432
|
1.5814597
|
1.6259222
|
1.6660457
|
1.6941691
|
1.7063527
|
1.7177220
|
1.7584874
|
1.7871770
|
2.5412846
|
2.2836974
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.1080114
|
1.1577576
|
1.2617732
|
1.3216870
|
1.3061257
|
1.3350413
|
1.4266053
|
1.5065665
|
1.5969981
|
1.6679795
|
1.7271516
|
1.7863690
|
3.155017
|
3.0403847
|
2.7014297
|
2.8771479
|
2.8360397
|
2.9954009
|
2.9642977
|
2.8963161
|
2.8985423
|
2.8660181
|
2.9238211
|
2.9203170
|
1.101310e+00
|
1.3801037
|
1.3854369
|
1.5228097
|
1.5577902
|
1.5402714
|
1.5416878
|
1.5752095
|
1.5914634
|
1.6536299
|
1.7103440
|
1.7389881
|
1.2338905
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.2181696
|
1.7799896
|
2.0477150
|
2.4283604
|
2.7262225
|
2.8266447
|
2.9445809
|
2.9977380
|
3.0762501
|
3.1410134
|
3.1891000
|
3.2305113
|
2.994324e+00
|
1.2209823
|
2.9029557
|
-3.791015e-01
|
7.401473e-01
|
1.855474e+00
|
3.035202e+00
|
3.0858336
|
3.0978952
|
2.04362880
|
3.0042736
|
2.972696e+00
|
1.1350347
|
1.5327785
|
1.7377441
|
2.0966865
|
2.4001221
|
2.5704244
|
2.7177394
|
2.8033499
|
2.8996504
|
2.9787477
|
3.0430483
|
3.0968104
|
3.179249e+00
|
1.6499882
|
3.0000000
|
0.000000e+00
|
1.000000e+00
|
2.000000e+00
|
3.000000e+00
|
3.0000000
|
3.000000
|
2.00000000
|
3.000000
|
3.000000e+00
|
1.4497370
|
1.6277858
|
1.5926255
|
1.7518485
|
1.9328607
|
2.0567934
|
2.2023114
|
2.2566548
|
2.3239697
|
2.3684895
|
2.3951467
|
2.4228990
|
2.07690000
|
1.2532000
|
2.8436333
|
1.09120000
|
1.2532000
|
2.07690000
|
2.9196333
|
2.9196333
|
2.9196333
|
2.0769000
|
2.9196333
|
2.91963333
|
1.0868414
|
1.2662052
|
1.3412578
|
1.4423413
|
1.5214453
|
1.5819541
|
1.6328541
|
1.6709791
|
1.6955640
|
1.7151637
|
1.7634119
|
1.7952930
|
1.85748712
|
1.8507280
|
1.8504071
|
1.8503919
|
1.8503912
|
1.8503911
|
1.8503911
|
1.8503911
|
1.8503911
|
1.85039114
|
1.8503911
|
1.8503911
|
3
|
4.0
|
3
|
4
|
4
|
3
|
1.0
|
3.0
|
1.0
|
3
|
4
|
2.0
|
0
|
Same
|
5
|
Inconclusive
|
0
|
Same
|
ARMA_1_ASE
|
ARI_2_ASE
|
ARI_3_ASE
|
ARI_4_ASE
|
ARI_5_ASE
|
ARI_6_ASE
|
ARI_7_ASE
|
ARI_8_ASE
|
ARIMA_9_ASE
|
ARIMA_10_ASE
|
ARIMA_11_ASE
|
ARIMA_12_ASE
|
|
701001768
|
TAAKA VODKA 80 1L
|
701001768
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.48537100
|
0.10000000
|
inconclusive
|
4.8252590
|
4.7309000
|
4.6891906
|
4.6548744
|
4.4625923
|
4.1009000
|
3.8908267
|
3.6955154
|
3.6434926
|
3.5806949
|
3.4830212
|
3.3908573
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
5.7808807
|
5.7352446
|
5.5232941
|
5.3965745
|
5.0694056
|
4.6852788
|
4.3741196
|
4.0734054
|
3.9675615
|
3.8590111
|
3.7221682
|
3.5847486
|
5.6248910
|
5.4467598
|
5.2805248
|
5.1628121
|
5.0707310
|
5.0013829
|
4.9484091
|
4.9081590
|
4.8775150
|
4.8542023
|
4.8364617
|
4.8229630
|
5.2605927
|
5.2971241
|
5.2878247
|
5.1661669
|
4.8449283
|
4.4788421
|
4.1976426
|
3.9212539
|
3.8133806
|
3.6862767
|
3.5379249
|
3.3946633
|
5.2292753
|
5.1602537
|
5.1018358
|
5.0523925
|
5.0105452
|
4.9751268
|
4.9451497
|
4.9197779
|
4.8983040
|
4.8801291
|
4.8647464
|
4.8517269
|
6.4952601
|
6.7509692
|
6.8814484
|
6.9519907
|
6.6458896
|
6.4373570
|
6.0746771
|
5.6287433
|
5.4702737
|
5.2501518
|
5.0207343
|
4.7790625
|
5.007063
|
5.4635630
|
5.5001693
|
5.3700205
|
5.4207692
|
5.4297450
|
5.4130208
|
5.4184816
|
5.4201225
|
5.4180126
|
5.4185753
|
5.4188403
|
5.732139e+00
|
5.7316454
|
5.9547820
|
5.9448081
|
5.6364189
|
5.3917974
|
5.0619394
|
4.6936530
|
4.5424800
|
4.3541588
|
4.1646626
|
3.9649667
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0628519
|
4.8568522
|
4.9092401
|
5.1366898
|
4.8822748
|
4.7841646
|
4.6868093
|
4.5409002
|
4.5063650
|
4.5045398
|
4.5556105
|
4.6106155
|
5.094327e+00
|
5.8633772
|
6.5140523
|
9.270669e+00
|
1.053951e+00
|
3.877910e+00
|
2.730153e+00
|
3.6076001
|
3.5055162
|
2.42061398
|
6.3499621
|
6.291181e+00
|
4.9079529
|
4.9796347
|
5.0235500
|
5.3625069
|
5.1204962
|
4.9727518
|
4.8140382
|
4.7272246
|
4.7013188
|
4.6820765
|
4.7368283
|
4.7764556
|
4.750496e+00
|
5.5412219
|
6.3569667
|
9.194740e+00
|
1.051907e+00
|
3.926150e+00
|
2.815428e+00
|
3.7179421
|
3.632111
|
2.55654143
|
6.490006
|
6.431425e+00
|
4.3015800
|
4.2148830
|
4.1777067
|
4.2515324
|
4.2137678
|
3.9471129
|
3.7287577
|
3.5048549
|
3.3129194
|
3.1993758
|
3.0721182
|
2.9674082
|
2.95280000
|
3.8222000
|
4.4437000
|
6.68485333
|
2.5756333
|
3.06963333
|
2.6436333
|
3.0696333
|
3.0696333
|
2.6436333
|
5.3402333
|
5.34023333
|
4.4760203
|
5.5609093
|
6.1915751
|
7.4413004
|
7.3220297
|
7.3204154
|
7.3389896
|
7.2958025
|
7.2740405
|
7.3399115
|
7.4404394
|
7.3617442
|
4.05054668
|
5.0112628
|
5.0765632
|
5.0899045
|
4.9679198
|
4.7096879
|
4.8428403
|
4.1826157
|
4.7815746
|
3.62254841
|
4.6861089
|
4.0379549
|
4
|
5.0
|
4
|
5
|
4
|
3
|
2.0
|
3.0
|
1.0
|
5
|
4
|
4.0
|
3
|
Same
|
3
|
Same
|
3
|
Same
|
RF_1_ASE
|
RF_2_ASE
|
RF_3_ASE
|
RF_4_ASE
|
RF_5_ASE
|
RF_6_ASE
|
RF_7_ASE
|
RF_8_ASE
|
RF_9_ASE
|
RF_10_ASE
|
RF_11_ASE
|
RF_12_ASE
|
|
700005850
|
TAAKA VODKA 80 1L
|
700005850
|
7.619964e-01
|
5.395173e-01
|
white noise
|
white noise
|
0.05366716
|
0.10000000
|
inconclusive
|
0.7946959
|
0.8499524
|
0.8584566
|
0.8650806
|
0.8504908
|
0.8458498
|
0.8291648
|
0.8163306
|
0.8088270
|
0.8013113
|
0.7960479
|
0.7916831
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
0.8274480
|
0.8556140
|
0.8596201
|
0.8577027
|
0.8435695
|
0.8405124
|
0.8198949
|
0.8073862
|
0.8030721
|
0.7959836
|
0.7909743
|
0.7861360
|
1.3037058
|
1.7600904
|
1.6373010
|
1.6703373
|
1.6614489
|
1.6638403
|
1.6631969
|
1.6633700
|
1.6633235
|
1.6633360
|
1.6633326
|
1.6633335
|
0.9313236
|
0.9150902
|
0.8734208
|
0.8846560
|
0.8540705
|
0.8458284
|
0.8180487
|
0.7992410
|
0.8014471
|
0.7953921
|
0.7858098
|
0.7843329
|
1.1990399
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
0.7136781
|
0.7603013
|
0.7850604
|
0.7767056
|
0.7513776
|
0.7727133
|
0.7613526
|
0.7442265
|
0.7436889
|
0.7651053
|
0.7828025
|
0.8060640
|
1.627028
|
1.8024442
|
2.0871018
|
2.2147253
|
2.2693248
|
1.8223728
|
2.0396027
|
2.1059611
|
2.1475281
|
2.0723239
|
1.9786281
|
2.0860657
|
1.668124e+00
|
1.7099588
|
1.4339802
|
1.4716291
|
1.4078535
|
1.4279774
|
1.3607470
|
1.3316285
|
1.2889480
|
1.2815764
|
1.2452635
|
1.2593410
|
-0.2756279
|
0.6273774
|
0.3784424
|
0.4470673
|
0.4281492
|
0.4333644
|
0.4319267
|
0.4323230
|
0.4322138
|
0.4322439
|
0.4322356
|
0.4322379
|
2.1899248
|
2.3685072
|
2.4259557
|
2.4526570
|
2.3475776
|
2.3281706
|
2.3142204
|
2.2941715
|
2.2700343
|
2.2584155
|
2.2489090
|
2.2409872
|
1.376305e+00
|
1.1944979
|
2.9393463
|
1.891471e-02
|
1.994101e+00
|
3.001839e+00
|
1.999426e+00
|
2.0001789
|
0.9999442
|
2.00001740
|
1.9999946
|
3.000002e+00
|
2.2656312
|
2.5005957
|
2.5645920
|
2.5893030
|
2.5160865
|
2.4683782
|
2.4140848
|
2.3876108
|
2.3643841
|
2.3484751
|
2.3305555
|
2.3184846
|
1.250166e+00
|
1.0000000
|
3.0000000
|
0.000000e+00
|
2.000000e+00
|
3.000000e+00
|
2.000000e+00
|
2.0000000
|
1.000000
|
2.00000000
|
2.000000
|
3.000000e+00
|
1.5086151
|
1.7295398
|
1.8467675
|
1.8756080
|
1.8194827
|
1.7965394
|
1.7867383
|
1.7965108
|
1.7917820
|
1.7672508
|
1.7732442
|
1.7652827
|
2.07492667
|
1.1029667
|
2.5915000
|
0.62606667
|
2.0749267
|
2.59150000
|
2.0749267
|
2.0749267
|
1.1029667
|
2.0749267
|
2.0749267
|
2.59150000
|
0.7469871
|
0.8089189
|
0.8212921
|
0.8196412
|
0.8007555
|
0.7889079
|
0.7727866
|
0.7617197
|
0.7543529
|
0.7516042
|
0.7500465
|
0.7519794
|
1.67447371
|
1.6753555
|
1.6761101
|
1.6761764
|
1.6761825
|
1.6761831
|
1.6761832
|
1.6761832
|
1.6761832
|
1.67618318
|
1.6761832
|
1.6761832
|
1
|
3.0
|
2
|
3
|
3
|
1
|
1.0
|
2.0
|
1.0
|
1
|
1
|
2.0
|
1
|
Same
|
3
|
Same
|
0
|
Same
|
ARI_1_ASE
|
ARI_2_ASE
|
ARI_3_ASE
|
ARI_4_ASE
|
ARI_5_ASE
|
ARI_6_ASE
|
ARI_7_ASE
|
ARI_8_ASE
|
ARI_9_ASE
|
MLP_10_ASE
|
MLP_11_ASE
|
MLP_12_ASE
|
|
701001830
|
TAAKA VODKA 80 1L
|
701001830
|
3.036074e-07
|
1.098736e-05
|
not white noise
|
NA
|
0.01094855
|
0.01000000
|
inconclusive
|
3.3812607
|
3.3274145
|
3.5419444
|
3.2107479
|
2.9817735
|
2.8650214
|
2.8658761
|
2.8812607
|
2.8883832
|
2.8969017
|
2.9194891
|
2.9395940
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
4.1195559
|
3.5713366
|
3.7072977
|
3.3318504
|
3.0777489
|
2.9445599
|
2.9342083
|
2.9409849
|
2.9414952
|
2.9446762
|
2.9629247
|
2.9794081
|
2.3499644
|
2.1716139
|
2.1523362
|
2.1502525
|
2.1500273
|
2.1500030
|
2.1500003
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
4.0075461
|
3.4573960
|
3.5632091
|
3.2119952
|
2.9633191
|
2.8283792
|
2.8212973
|
2.8343839
|
2.8403766
|
2.8490921
|
2.8750325
|
2.8932145
|
2.6117949
|
2.5657214
|
2.5242446
|
2.4869061
|
2.4532928
|
2.4230331
|
2.3957925
|
2.3712696
|
2.3491934
|
2.3293198
|
2.3114290
|
2.2953231
|
4.4124645
|
3.7578602
|
3.8341698
|
3.3643326
|
2.9092633
|
2.6517494
|
2.5413667
|
2.4553315
|
2.3413992
|
2.2748029
|
2.2158203
|
2.1626971
|
2.923598
|
3.1198527
|
3.2635508
|
3.2747402
|
3.1578870
|
3.2178726
|
3.2220501
|
3.2142954
|
3.2063478
|
3.2155823
|
3.2134327
|
3.2124481
|
5.693141e+00
|
5.0198774
|
5.5617559
|
5.3414683
|
5.1786422
|
5.1347258
|
5.2635862
|
5.2865471
|
5.2943238
|
5.2846495
|
5.1757291
|
5.0886369
|
1.3356365
|
1.5349088
|
1.2718390
|
1.1519197
|
1.0750150
|
1.0476987
|
1.0247556
|
1.0139917
|
1.0077459
|
1.0043690
|
1.0024185
|
1.0013730
|
4.6622079
|
4.5027535
|
4.5786104
|
4.2220598
|
4.0085668
|
3.8276560
|
3.7973333
|
3.7649766
|
3.7141669
|
3.6735195
|
3.6612415
|
3.6510099
|
4.134380e+00
|
2.0090290
|
7.0006067
|
4.076102e-05
|
3.000003e+00
|
3.000000e+00
|
2.000000e+00
|
3.0000000
|
3.0000000
|
2.00000000
|
3.0000000
|
4.000000e+00
|
4.7621704
|
4.6118544
|
4.6386722
|
4.2674657
|
4.0447418
|
3.8577976
|
3.8231672
|
3.7875790
|
3.7342582
|
3.6916017
|
3.6776798
|
3.6660783
|
4.000000e+00
|
2.0000000
|
7.0000000
|
0.000000e+00
|
3.000000e+00
|
3.000000e+00
|
2.000000e+00
|
3.0000000
|
3.000000
|
2.00000000
|
3.000000
|
4.000000e+00
|
3.8952434
|
3.9804560
|
4.0401597
|
3.6758753
|
3.3930295
|
3.2010656
|
3.1777504
|
3.1670737
|
3.1422030
|
3.1199983
|
3.1416187
|
3.1438423
|
3.50236667
|
1.9997667
|
3.5023667
|
0.96713333
|
3.0698667
|
3.06986667
|
1.9997667
|
3.0698667
|
3.0698667
|
1.9997667
|
3.0698667
|
3.50236667
|
2.7212150
|
2.6266601
|
2.6485940
|
2.4661611
|
2.2155598
|
2.0311468
|
1.8818072
|
1.7747177
|
1.6605236
|
1.5777955
|
1.5215492
|
1.5191932
|
1.96478139
|
2.6750292
|
3.3752563
|
2.4040338
|
2.6981055
|
2.7940376
|
2.7127253
|
2.8671529
|
2.0644713
|
2.95250704
|
2.6267476
|
1.8080634
|
3
|
4.0
|
5
|
4
|
3
|
4
|
5.0
|
3.0
|
2.0
|
3
|
1
|
3.0
|
0
|
Same
|
10
|
Different
|
0
|
Same
|
MLP_1_ASE
|
MLP_2_ASE
|
MLP_3_ASE
|
MLP_4_ASE
|
MLP_5_ASE
|
MLP_6_ASE
|
MLP_7_ASE
|
MLP_8_ASE
|
MLP_9_ASE
|
MLP_10_ASE
|
MLP_11_ASE
|
MLP_12_ASE
|
|
701001767
|
TAAKA VODKA 80 1L
|
701001767
|
1.572929e-01
|
3.914269e-01
|
white noise
|
white noise
|
0.34274666
|
0.10000000
|
inconclusive
|
1.3459188
|
1.3023291
|
1.2066026
|
1.0946368
|
1.0100214
|
0.9544658
|
0.9140507
|
0.8667521
|
0.8387963
|
0.8400214
|
0.8181799
|
0.7995513
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
1.6849370
|
1.5191555
|
1.4122413
|
1.3255207
|
1.2404640
|
1.1440327
|
1.0691724
|
1.0128380
|
0.9728313
|
0.9649127
|
0.9322498
|
0.9059205
|
2.3807007
|
2.1586414
|
2.9171391
|
2.5688481
|
2.2957120
|
2.3980851
|
2.2483458
|
2.5293585
|
2.3935057
|
2.3247667
|
2.3487997
|
2.2723652
|
1.9333380
|
1.6235733
|
1.4734965
|
1.3692537
|
1.2889112
|
1.1850200
|
1.1032948
|
1.0376552
|
0.9962394
|
0.9859819
|
0.9518440
|
0.9239290
|
2.3807007
|
2.1586414
|
2.9171391
|
2.5688481
|
2.2957120
|
2.3980851
|
2.2483458
|
2.5293585
|
2.3935057
|
2.3247667
|
2.3487997
|
2.2723652
|
1.8476868
|
1.6458625
|
1.5243294
|
1.4577167
|
1.3671893
|
1.2673377
|
1.1895293
|
1.1448096
|
1.1118251
|
1.0889661
|
1.0359676
|
1.0036943
|
2.467270
|
2.3263118
|
3.1237031
|
2.7179263
|
2.4276452
|
2.5866173
|
2.4886757
|
2.8069577
|
2.6373311
|
2.5623943
|
2.6135525
|
2.5630514
|
1.750115e+00
|
1.6015794
|
1.4961143
|
1.4375831
|
1.3589958
|
1.2541742
|
1.1713232
|
1.1220013
|
1.0900889
|
1.0678670
|
1.0191809
|
0.9856034
|
2.4672700
|
2.3263118
|
3.1237031
|
2.7179263
|
2.4276452
|
2.5866173
|
2.4886757
|
2.8069577
|
2.6373311
|
2.5623943
|
2.6135525
|
2.5630514
|
2.0954669
|
2.3354071
|
2.3971244
|
2.3672999
|
2.2354135
|
2.1887884
|
2.1395257
|
2.0666908
|
2.0105044
|
1.9518243
|
1.9235242
|
1.9086431
|
1.388334e+00
|
-0.1222036
|
4.3714579
|
9.221621e-01
|
1.751937e+00
|
2.126097e+00
|
9.330889e-01
|
2.1345194
|
1.9482754
|
3.94710235
|
3.0344675
|
1.974070e+00
|
3.5248317
|
3.1975521
|
3.0058058
|
2.8039407
|
2.5891514
|
2.4424681
|
2.3937548
|
2.2516280
|
2.1680245
|
2.1171107
|
2.0662856
|
2.0406461
|
1.388334e+00
|
-0.1222036
|
4.3714579
|
9.221621e-01
|
1.751937e+00
|
2.126097e+00
|
9.330889e-01
|
2.1345194
|
1.948275
|
3.94710235
|
3.034467
|
1.974070e+00
|
2.2752853
|
2.2865714
|
2.1463304
|
2.0546172
|
1.9337087
|
1.8137312
|
1.7312740
|
1.6297377
|
1.5540143
|
1.4948290
|
1.4447854
|
1.4080131
|
1.54346667
|
1.4094667
|
3.7699333
|
1.54746667
|
2.0338667
|
2.03386667
|
1.5474667
|
2.0338667
|
2.0338667
|
3.8159333
|
2.9889667
|
2.03386667
|
1.5974004
|
1.5874738
|
1.4590806
|
1.3691841
|
1.2766082
|
1.2157134
|
1.1728828
|
1.1201923
|
1.0974608
|
1.0958563
|
1.0791525
|
1.0664492
|
2.51795322
|
2.5816271
|
2.5215890
|
2.5436836
|
2.4931962
|
2.5421304
|
2.4840087
|
2.5420571
|
2.4805325
|
2.54205348
|
2.4788255
|
2.5420533
|
1
|
2.0
|
1
|
2
|
2
|
2
|
2.0
|
2.0
|
1.0
|
4
|
2
|
3.0
|
0
|
Same
|
0
|
Same
|
0
|
Same
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
|
701001810
|
TAAKA VODKA 80 1L
|
701001810
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.54437889
|
0.01060199
|
not stationary
|
2.7694231
|
2.9386538
|
3.0241239
|
2.5476282
|
2.2068590
|
1.9775427
|
1.8086172
|
1.6787179
|
1.5862322
|
1.5060897
|
1.4374883
|
1.3850214
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
3.8104860
|
3.4868913
|
3.3838308
|
2.9336535
|
2.5560414
|
2.3282849
|
2.1275674
|
1.9714185
|
1.8694536
|
1.8120156
|
1.7505586
|
1.6899983
|
2.0522846
|
2.0606613
|
2.0716803
|
2.0910025
|
2.0987020
|
2.1068851
|
2.1155365
|
2.1207943
|
2.1257431
|
2.1301576
|
2.1333904
|
2.1362404
|
4.5910109
|
4.3387866
|
4.0374311
|
3.4712152
|
3.0265545
|
2.7291639
|
2.5120868
|
2.3031515
|
2.1768817
|
2.0924796
|
1.9910859
|
1.8807362
|
1.9994117
|
2.0113054
|
2.0222596
|
2.0323487
|
2.0416410
|
2.0501993
|
2.0580817
|
2.0653416
|
2.0720280
|
2.0781863
|
2.0838583
|
2.0890822
|
4.9419848
|
4.5273245
|
4.3249139
|
3.8658440
|
3.4547432
|
3.1730843
|
2.9767132
|
2.7948007
|
2.6591141
|
2.6571698
|
2.5919980
|
2.5046672
|
2.000000
|
2.0000000
|
2.0000000
|
2.0000000
|
2.0000000
|
2.0000000
|
2.0000000
|
2.0000000
|
2.0000000
|
2.0000000
|
2.0000000
|
2.0000000
|
4.163263e+00
|
4.1005963
|
3.8469651
|
3.5846134
|
3.2781221
|
3.0622591
|
2.8507899
|
2.7383287
|
2.5955131
|
2.6034540
|
2.5522387
|
2.4843749
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
4.5007056
|
4.2145008
|
4.9602165
|
5.5237227
|
5.5113092
|
5.3473846
|
5.2607772
|
5.1530926
|
5.0493986
|
4.9398601
|
4.8464986
|
4.7364310
|
1.754320e+00
|
1.8136830
|
6.6689289
|
-1.672947e-01
|
1.869808e+00
|
1.862734e+00
|
1.907326e+00
|
1.9268275
|
0.9354361
|
1.95134954
|
1.9613037
|
1.967943e+00
|
4.2968421
|
4.2911908
|
4.8025444
|
5.3729971
|
5.2736339
|
5.1200821
|
5.0757839
|
4.9578981
|
4.8594204
|
4.7532709
|
4.6769437
|
4.5169437
|
1.511649e+00
|
1.5856716
|
6.6484741
|
-2.982427e-01
|
1.746964e+00
|
1.785318e+00
|
1.817859e+00
|
1.8454674
|
0.868891
|
1.88876405
|
1.905625
|
1.919930e+00
|
4.2627223
|
4.3938975
|
4.4888107
|
4.3204152
|
4.0250354
|
3.8316310
|
3.6507450
|
3.5194287
|
3.4388163
|
3.3199221
|
3.2178539
|
3.1353280
|
2.13353333
|
2.1335333
|
3.8364667
|
1.67553333
|
2.1335333
|
2.13353333
|
2.1335333
|
2.1335333
|
1.7475333
|
2.1335333
|
2.1335333
|
2.13353333
|
6.2323161
|
6.3950550
|
6.3344517
|
6.4902739
|
6.2317929
|
6.0053320
|
5.9584333
|
5.8583975
|
5.8103266
|
5.7817543
|
5.7268943
|
5.7062041
|
3.78075129
|
3.8335219
|
3.3440681
|
3.7790476
|
3.7876858
|
3.4186161
|
3.7704002
|
3.7543244
|
3.5564493
|
3.76545494
|
3.7096241
|
3.5972262
|
0
|
0.0
|
1
|
2
|
2
|
2
|
2.0
|
2.0
|
1.0
|
2
|
2
|
1.0
|
3
|
Same
|
4
|
Same
|
0
|
Same
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
|
700005866
|
TAAKA VODKA 80 1L
|
700005866
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.31221025
|
0.03174915
|
not stationary
|
41.0203241
|
39.3216062
|
37.4481019
|
36.1557088
|
34.5091447
|
32.8639994
|
31.5960751
|
30.2733370
|
28.8379310
|
27.3446318
|
25.8320957
|
24.2926318
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
2.1423171
|
2.3126465
|
2.4994165
|
2.8478203
|
3.0488383
|
3.2621905
|
4.0197278
|
4.6931444
|
5.0472028
|
5.2261957
|
5.2745856
|
5.1754304
|
3.1421683
|
4.7655097
|
4.2451054
|
4.9756542
|
4.9146008
|
5.2965478
|
5.3803732
|
5.6156141
|
5.7338443
|
5.8991854
|
6.0148171
|
6.1410207
|
2.0775360
|
2.3394179
|
2.5571922
|
2.9196070
|
3.1780195
|
3.4797540
|
4.2895843
|
5.0141588
|
5.4157855
|
5.5611932
|
5.6039443
|
5.5032397
|
3.1421683
|
4.7655097
|
4.2451054
|
4.9756542
|
4.9146008
|
5.2965478
|
5.3803732
|
5.6156141
|
5.7338443
|
5.8991854
|
6.0148171
|
6.1410207
|
2.2144318
|
2.4734601
|
2.8964279
|
3.4077682
|
4.0111561
|
4.7870135
|
6.4359796
|
8.1630524
|
9.6417922
|
11.0790659
|
12.4832663
|
13.8160406
|
2.515230
|
4.0587504
|
3.0999274
|
3.6955410
|
3.3255503
|
3.5553857
|
3.4126137
|
3.5013026
|
3.4462097
|
3.4804330
|
3.4591738
|
3.4723798
|
2.214432e+00
|
2.4734601
|
2.8964279
|
3.4077682
|
4.0111561
|
4.7870135
|
6.4359796
|
8.1630524
|
9.6417922
|
11.0790659
|
12.4832663
|
13.8160406
|
2.5152302
|
4.0587504
|
3.0999274
|
3.6955410
|
3.3255503
|
3.5553857
|
3.4126137
|
3.5013026
|
3.4462097
|
3.4804330
|
3.4591738
|
3.4723798
|
10.2760336
|
10.0534371
|
11.9321992
|
14.0501086
|
16.4457911
|
18.6997705
|
21.5815729
|
24.1789929
|
26.3722464
|
28.4575525
|
30.5286043
|
32.1550920
|
6.541185e-01
|
2.0293576
|
3.2816330
|
8.918506e-01
|
1.890484e+00
|
2.598173e+00
|
1.513890e+00
|
0.4059347
|
1.3144753
|
1.25664156
|
1.2006290
|
5.160187e+00
|
9.2666327
|
8.9026054
|
10.6855680
|
13.0907783
|
15.4853964
|
17.6714293
|
20.6486179
|
23.4194441
|
25.6164912
|
27.6086889
|
29.5849732
|
31.2513160
|
6.541185e-01
|
2.0293576
|
3.2816330
|
8.918506e-01
|
1.890484e+00
|
2.598173e+00
|
1.513890e+00
|
0.4059347
|
1.314475
|
1.25664156
|
1.200629
|
5.160187e+00
|
22.6690955
|
22.3706392
|
21.3931192
|
20.2447847
|
19.6278890
|
18.8959654
|
19.2803284
|
19.4021001
|
19.0556658
|
18.7982994
|
18.4446686
|
18.0697664
|
0.20270000
|
0.2027000
|
0.7374667
|
0.20270000
|
0.2847000
|
0.73746667
|
0.2847000
|
0.2027000
|
0.2847000
|
0.2847000
|
0.2847000
|
2.72645333
|
11.3669222
|
19.5649319
|
18.4826638
|
20.4238714
|
19.8523646
|
20.4940658
|
19.8875187
|
20.0306814
|
19.5107474
|
19.2391295
|
18.3524754
|
17.5890176
|
6.83278071
|
7.6198127
|
9.2888563
|
8.2992533
|
8.9360892
|
8.7086563
|
8.7556370
|
8.4971381
|
8.6007666
|
8.56988634
|
8.8917538
|
8.8564703
|
4
|
4.0
|
5
|
4
|
7
|
7
|
12.0
|
5.0
|
4.0
|
7
|
6
|
7.0
|
12
|
Different
|
8
|
Inconclusive
|
5
|
Inconclusive
|
ARMA_1_ASE
|
AR_2_ASE
|
AR_3_ASE
|
AR_4_ASE
|
AR_5_ASE
|
AR_6_ASE
|
AR_7_ASE
|
AR_8_ASE
|
AR_9_ASE
|
AR_10_ASE
|
AR_11_ASE
|
AR_12_ASE
|
|
701001880
|
TAAKA VODKA 80 1L
|
701001880
|
3.857241e-01
|
7.674317e-01
|
white noise
|
white noise
|
0.02966138
|
0.01000000
|
inconclusive
|
1.4269017
|
1.4461325
|
1.4610897
|
1.4794658
|
1.4438248
|
1.4059615
|
1.3686600
|
1.3381197
|
1.3157906
|
1.2974145
|
1.2604681
|
1.1828846
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.4632116
|
1.4606273
|
1.4694321
|
1.4854492
|
1.4485794
|
1.4099279
|
1.3720603
|
1.3410947
|
1.3184351
|
1.2997946
|
1.2626319
|
1.1848680
|
1.3022427
|
1.3553473
|
1.3646778
|
1.3663172
|
1.3666053
|
1.3666559
|
1.3666648
|
1.3666663
|
1.3666666
|
1.3666667
|
1.3666667
|
1.3666667
|
1.5174396
|
1.4948883
|
1.4825224
|
1.5006991
|
1.4644323
|
1.4215440
|
1.3823838
|
1.3492160
|
1.3240311
|
1.3028449
|
1.2655291
|
1.1871722
|
1.1998213
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.8877476
|
1.9194500
|
1.8957387
|
1.8800721
|
1.9207119
|
1.9062499
|
1.8450005
|
1.8043792
|
1.7733499
|
1.7299787
|
1.6582344
|
1.5742589
|
2.265142
|
2.8990216
|
2.4129341
|
2.0937681
|
2.4508967
|
2.4996365
|
2.3321244
|
2.3382486
|
2.4198043
|
2.3981070
|
2.3645226
|
2.3816840
|
2.006564e+00
|
2.3342891
|
2.3004118
|
2.3140128
|
2.2600330
|
2.2419669
|
2.2286639
|
2.2570145
|
2.3453982
|
2.3518083
|
2.3005831
|
2.2235996
|
2.2651424
|
2.8990216
|
2.4129341
|
2.0937681
|
2.4508967
|
2.4996365
|
2.3321244
|
2.3382486
|
2.4198043
|
2.3981070
|
2.3645226
|
2.3816840
|
2.1615656
|
2.1667482
|
1.9967441
|
1.9027505
|
1.8423261
|
1.8024660
|
1.7306281
|
1.6653109
|
1.6169190
|
1.5865805
|
1.5956469
|
1.5843790
|
7.701703e-01
|
1.7905603
|
1.0171284
|
2.734544e+00
|
1.491103e-01
|
1.227693e-01
|
1.921934e+00
|
0.9883702
|
2.0535955
|
2.99700475
|
3.9818192
|
1.011102e+00
|
3.1199556
|
3.1503628
|
2.7148049
|
2.6544759
|
2.6625206
|
2.6213930
|
2.4272105
|
2.3303058
|
2.2490123
|
2.1530840
|
2.0921738
|
2.0336181
|
6.240092e-01
|
1.3893508
|
0.4234414
|
1.629559e+00
|
-6.083827e-01
|
-5.406915e-01
|
9.984264e-01
|
0.1504591
|
1.624386
|
2.57950379
|
3.559803
|
8.524046e-01
|
1.7367648
|
1.7354995
|
1.7805959
|
1.7941530
|
1.7085816
|
1.6483342
|
1.5562342
|
1.4661224
|
1.4074715
|
1.3773042
|
1.3301063
|
1.2688826
|
1.10096667
|
1.1009667
|
1.1009667
|
2.47486667
|
0.8509667
|
0.85096667
|
2.0128667
|
1.1009667
|
2.0128667
|
2.4748667
|
2.4748667
|
1.10096667
|
1.6330388
|
1.6652712
|
1.6929307
|
1.7260548
|
1.6901861
|
1.6519590
|
1.6222425
|
1.5906834
|
1.5655486
|
1.5449177
|
1.5029218
|
1.4174294
|
1.06486765
|
1.0733548
|
1.0744504
|
1.0745917
|
1.0746099
|
1.0746122
|
1.0746125
|
1.0746125
|
1.0746126
|
1.07461255
|
1.0746126
|
1.0746126
|
2
|
2.0
|
2
|
2
|
1
|
1
|
2.0
|
1.0
|
1.0
|
2
|
1
|
2.0
|
1
|
Same
|
1
|
Same
|
0
|
Same
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
|
700005861
|
TAAKA VODKA 80 1L
|
700005861
|
2.798171e-05
|
8.768368e-04
|
not white noise
|
NA
|
0.11665129
|
0.04796742
|
not stationary
|
3.1449053
|
3.4033669
|
3.4895207
|
3.0920848
|
2.8322387
|
2.6534524
|
2.5257478
|
2.4283669
|
2.3526261
|
2.2995464
|
2.2598471
|
2.2449908
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
3.6103811
|
3.7400735
|
3.8738059
|
3.5320257
|
3.2876049
|
3.0869508
|
2.9045681
|
2.7381857
|
2.5404546
|
2.4204425
|
2.3236917
|
2.2851496
|
2.1362137
|
2.1503921
|
1.7043704
|
1.5957308
|
1.4608462
|
1.4007512
|
1.3531158
|
1.3266516
|
1.3085121
|
1.2975709
|
1.2904464
|
1.2860171
|
2.9120469
|
3.2251489
|
3.3751316
|
3.1821920
|
2.9660736
|
2.8280312
|
2.6700627
|
2.5279470
|
2.3702667
|
2.2522066
|
2.1634190
|
2.1149156
|
1.9633104
|
1.8684188
|
1.7866728
|
1.7162512
|
1.6555854
|
1.6033237
|
1.5583020
|
1.5195172
|
1.4861054
|
1.4573222
|
1.4325264
|
1.4111657
|
3.8023323
|
3.7834990
|
3.9717565
|
3.8815795
|
3.8733549
|
3.7546458
|
3.6083021
|
3.4169530
|
3.1325053
|
2.8819962
|
2.6890521
|
2.5379689
|
2.520297
|
2.9617768
|
3.0405952
|
2.8864131
|
2.9633104
|
2.9523481
|
2.9408165
|
2.9505872
|
2.9472727
|
2.9470001
|
2.9479596
|
2.9474291
|
2.962186e+00
|
3.4010894
|
3.5663049
|
3.4222635
|
3.2474177
|
3.0839616
|
2.9365710
|
2.7409038
|
2.5170765
|
2.3283588
|
2.1689325
|
2.0708163
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
6.5209704
|
5.8880700
|
5.8051970
|
5.2391593
|
4.7597069
|
4.4442414
|
4.1696337
|
3.8881701
|
3.6435928
|
3.4828183
|
3.3458965
|
3.2435679
|
2.581237e+00
|
3.1689182
|
6.0490907
|
1.426667e-02
|
2.004146e+00
|
1.001205e+00
|
1.000350e+00
|
1.0001018
|
1.0000296
|
1.00000860
|
2.0000025
|
4.000001e+00
|
6.0699551
|
5.8170034
|
5.8041211
|
5.1472329
|
4.7108524
|
4.4383617
|
4.1424992
|
3.8847447
|
3.6384809
|
3.4857644
|
3.3418270
|
3.2749200
|
2.156258e+00
|
3.1286190
|
6.1058690
|
8.714302e-02
|
2.071729e+00
|
1.059042e+00
|
1.048599e+00
|
1.0400025
|
1.032927
|
1.02710284
|
2.022309
|
4.018363e+00
|
3.3950610
|
3.5112456
|
3.5298015
|
2.9141782
|
2.5643647
|
2.3416000
|
2.1788045
|
2.0736032
|
1.9986188
|
1.9341835
|
1.8863442
|
1.8537934
|
2.17688000
|
2.5442500
|
2.5442500
|
0.86166667
|
2.1768800
|
1.42700000
|
1.4270000
|
1.4270000
|
1.4270000
|
1.4270000
|
2.1768800
|
2.54425000
|
5.5640833
|
4.8671587
|
4.8664444
|
4.2422822
|
3.9374958
|
3.6749217
|
3.5350968
|
3.3913569
|
3.3145945
|
3.2298250
|
3.1869215
|
3.1501618
|
0.29701584
|
0.9926585
|
0.7448190
|
0.8818302
|
0.8182658
|
0.8484444
|
0.8409626
|
0.8516034
|
0.8208837
|
0.86068583
|
0.8124858
|
0.8635693
|
3
|
4.0
|
3
|
2
|
1
|
2
|
1.0
|
1.0
|
1.0
|
2
|
1
|
3.0
|
2
|
Same
|
3
|
Same
|
0
|
Same
|
ARMA_1_ASE
|
ARMA_2_ASE
|
ARMA_3_ASE
|
RF_4_ASE
|
RF_5_ASE
|
RF_6_ASE
|
RF_7_ASE
|
RF_8_ASE
|
RF_9_ASE
|
RF_10_ASE
|
RF_11_ASE
|
RF_12_ASE
|
|
701001850
|
TAAKA VODKA 80 1L
|
701001850
|
3.111778e-09
|
1.111885e-09
|
not white noise
|
NA
|
0.01000000
|
0.01000000
|
inconclusive
|
1.6864744
|
1.6531410
|
1.6420299
|
1.6210897
|
1.6695513
|
1.6646795
|
1.6198077
|
1.6082692
|
1.5995798
|
1.5944231
|
1.5687587
|
1.5544231
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.2167612
|
1.3132914
|
1.3283362
|
1.3684879
|
1.4147987
|
1.3764311
|
1.3096835
|
1.2767104
|
1.2583194
|
1.2467150
|
1.2222427
|
1.2179406
|
0.9470581
|
1.4793827
|
0.9125766
|
1.4951652
|
1.3470116
|
1.2022296
|
1.5180307
|
1.3136316
|
1.4564144
|
1.5004856
|
1.4116544
|
1.5477606
|
0.9471051
|
0.9410728
|
0.9583839
|
0.9320713
|
1.0150731
|
1.0192416
|
0.9862771
|
0.9582185
|
0.9320831
|
0.9355995
|
0.9036402
|
0.8910035
|
0.6945360
|
1.3803241
|
0.8504484
|
1.1642663
|
1.0882857
|
1.0855645
|
1.1544780
|
1.1234983
|
1.1647393
|
1.1702968
|
1.1837013
|
1.2018058
|
0.6603510
|
0.6660583
|
0.6898079
|
0.7431496
|
0.7918948
|
0.7814434
|
0.7524623
|
0.7554578
|
0.7440502
|
0.7247367
|
0.6975496
|
0.6764127
|
0.751663
|
1.2945436
|
0.6991328
|
1.2715343
|
1.0815054
|
0.8580775
|
1.1862125
|
0.9383167
|
1.0657447
|
1.0882255
|
0.9535772
|
1.0939614
|
1.231704e+00
|
1.3687870
|
1.4382336
|
1.2802140
|
1.2448374
|
1.1547863
|
1.0938548
|
1.0693608
|
1.0896346
|
1.0644152
|
1.0024592
|
0.9485882
|
0.7516630
|
1.2945436
|
0.6991328
|
1.2715343
|
1.0815054
|
0.8580775
|
1.1862125
|
0.9383167
|
1.0657447
|
1.0882255
|
0.9535772
|
1.0939614
|
1.2290521
|
1.2306010
|
1.2574485
|
1.3136847
|
1.3280439
|
1.2604377
|
1.2139912
|
1.1989172
|
1.1884754
|
1.1820669
|
1.1774447
|
1.1588846
|
7.939023e-01
|
0.9616366
|
0.2798964
|
1.926051e+00
|
-3.873743e-02
|
1.106610e-01
|
9.188830e-01
|
0.9817491
|
1.0640390
|
-0.04758126
|
1.9989659
|
1.035383e+00
|
1.8266180
|
1.9893472
|
1.8271548
|
2.1182253
|
1.9236300
|
1.8966680
|
1.7713696
|
1.7231753
|
1.6788265
|
1.6423503
|
1.6187286
|
1.5899860
|
1.649683e+00
|
0.4027416
|
0.2461327
|
2.052215e+00
|
-1.627674e-01
|
1.252866e-01
|
9.607176e-01
|
0.9776946
|
1.038822
|
-0.02528883
|
2.005146
|
1.007060e+00
|
0.6625102
|
0.6887565
|
0.7526539
|
0.8496831
|
0.9215166
|
0.8913191
|
0.8472525
|
0.8163913
|
0.8152011
|
0.8225598
|
0.8185004
|
0.8080276
|
0.92503333
|
0.9250333
|
0.5430333
|
1.77556667
|
0.5430333
|
0.54303333
|
0.9250333
|
0.9250333
|
0.9250333
|
0.5430333
|
1.7755667
|
0.92503333
|
2.3060428
|
2.2514828
|
2.2333148
|
2.2245847
|
2.2964013
|
2.2995076
|
2.2461708
|
2.2402257
|
2.2354491
|
2.2314320
|
2.2038958
|
2.1905667
|
2.25170353
|
2.2576332
|
2.2580547
|
2.2580736
|
2.2580745
|
2.2580745
|
2.2580745
|
2.2580745
|
2.2580745
|
2.25807453
|
2.2580745
|
2.2580745
|
2
|
2.0
|
1
|
0
|
0
|
1
|
2.0
|
0.0
|
1.0
|
1
|
1
|
1.0
|
8
|
Inconclusive
|
13
|
Different
|
10
|
Different
|
ARI_1_ASE
|
ARI_2_ASE
|
ARI_3_ASE
|
ARI_4_ASE
|
ARI_5_ASE
|
ARI_6_ASE
|
ARI_7_ASE
|
ARI_8_ASE
|
ARI_9_ASE
|
ARI_10_ASE
|
ARI_11_ASE
|
ARI_12_ASE
|
|
701001901
|
TAAKA VODKA 80 1L
|
701001901
|
6.819545e-12
|
7.586510e-10
|
not white noise
|
NA
|
0.18541887
|
0.01000000
|
not stationary
|
0.7879701
|
1.0213034
|
1.1033547
|
1.1533547
|
1.1454060
|
1.1285684
|
1.1388858
|
1.1482265
|
1.1731553
|
1.1741239
|
1.1772475
|
1.1804915
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
0.9039698
|
1.0009117
|
1.0833437
|
1.0912622
|
1.0673421
|
1.0358710
|
1.0316080
|
1.0339897
|
1.0580836
|
1.0580962
|
1.0645334
|
1.0690368
|
1.7174987
|
1.6589596
|
1.5938820
|
1.5694253
|
1.5521604
|
1.5439991
|
1.5390721
|
1.5365215
|
1.5350696
|
1.5342906
|
1.5338571
|
1.5336213
|
0.7687278
|
0.8821437
|
0.9522771
|
0.9727396
|
0.9590509
|
0.9318883
|
0.9316880
|
0.9346157
|
0.9576802
|
0.9540655
|
0.9598836
|
0.9652515
|
1.8236440
|
1.7776671
|
1.7389717
|
1.7064044
|
1.6789949
|
1.6559263
|
1.6365111
|
1.6201707
|
1.6064181
|
1.5948436
|
1.5851021
|
1.5769034
|
1.0773801
|
1.0917869
|
1.1141590
|
1.1014160
|
1.0167743
|
0.9614797
|
0.9095619
|
0.8811259
|
0.8776666
|
0.8398308
|
0.8324013
|
0.8220713
|
2.325581
|
2.1020848
|
2.1121215
|
2.0976546
|
2.1759317
|
2.1253015
|
2.1302943
|
2.1236785
|
2.1425054
|
2.1308696
|
2.1327198
|
2.1304444
|
7.399023e-01
|
0.8315413
|
0.8883643
|
0.8961124
|
0.8765321
|
0.8336705
|
0.8133006
|
0.7998427
|
0.8038369
|
0.7852240
|
0.7804549
|
0.7791951
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
0.6268156
|
0.8059307
|
0.8477424
|
0.8884257
|
0.9611325
|
0.9841951
|
1.0054706
|
1.0130132
|
1.0219238
|
1.0221565
|
1.0136362
|
1.0150753
|
6.237002e-01
|
1.8583984
|
0.9467154
|
2.979949e+00
|
-7.545188e-03
|
1.997161e+00
|
1.998932e+00
|
1.9995980
|
2.9998487
|
1.99994307
|
1.9999786
|
1.999992e+00
|
0.6230959
|
0.8141515
|
0.8558566
|
0.9235350
|
0.9969168
|
1.0146397
|
1.0315406
|
1.0370816
|
1.0421818
|
1.0407774
|
1.0285619
|
1.0280019
|
6.237002e-01
|
1.8583984
|
0.9467154
|
2.979949e+00
|
-7.545188e-03
|
1.997161e+00
|
1.998932e+00
|
1.9995980
|
2.999849
|
1.99994307
|
1.999979
|
1.999992e+00
|
0.4516071
|
0.5422331
|
0.6095051
|
0.6583964
|
0.6915431
|
0.7051798
|
0.7134556
|
0.7207142
|
0.7240629
|
0.7150633
|
0.7002729
|
0.6970824
|
1.22433333
|
2.2006667
|
1.2243333
|
2.88866667
|
0.9903333
|
2.20066667
|
2.2006667
|
2.2006667
|
2.8886667
|
2.2006667
|
2.2006667
|
2.20066667
|
1.4285241
|
1.2525328
|
1.2396290
|
1.2201557
|
1.2152628
|
1.2451508
|
1.2653614
|
1.2636243
|
1.2851196
|
1.2548559
|
1.2700149
|
1.2769984
|
2.66121679
|
1.5791660
|
1.4907872
|
1.4465495
|
1.4687488
|
1.4483961
|
1.4562846
|
1.4450147
|
1.4241650
|
1.32618285
|
1.4275030
|
1.5844270
|
1
|
4.0
|
2
|
2
|
2
|
2
|
3.0
|
2.0
|
3.0
|
2
|
2
|
1.0
|
4
|
Same
|
11
|
Different
|
6
|
Inconclusive
|
RF_1_ASE
|
RF_2_ASE
|
RF_3_ASE
|
RF_4_ASE
|
RF_5_ASE
|
RF_6_ASE
|
RF_7_ASE
|
RF_8_ASE
|
RF_9_ASE
|
RF_10_ASE
|
RF_11_ASE
|
RF_12_ASE
|
|
701001904
|
TAAKA VODKA 80 1L
|
701001904
|
9.048454e-08
|
5.283449e-07
|
not white noise
|
NA
|
0.43473483
|
0.01000000
|
not stationary
|
1.1955769
|
1.2096795
|
1.2229274
|
1.2218590
|
1.2048077
|
1.1964316
|
1.1941117
|
1.1949359
|
1.1944373
|
1.1976282
|
1.2035023
|
1.2069017
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.2253128
|
1.2392695
|
1.2394043
|
1.2205312
|
1.2261086
|
1.2375913
|
1.2238957
|
1.2222534
|
1.2186818
|
1.2257325
|
1.2354346
|
1.2364830
|
1.7317351
|
2.2353856
|
2.6164106
|
1.7823203
|
1.8746877
|
2.0060020
|
1.6939037
|
1.6892508
|
1.7278686
|
1.6077762
|
1.5892680
|
1.5967780
|
1.4841602
|
1.5389074
|
1.4902713
|
1.4144033
|
1.3738584
|
1.3599818
|
1.3405575
|
1.3257438
|
1.3175436
|
1.3013227
|
1.3052061
|
1.3102041
|
1.5810320
|
2.0646698
|
2.6573867
|
1.9097428
|
1.9640995
|
1.9062874
|
1.9416191
|
1.7303295
|
1.7743717
|
1.7186676
|
1.6788633
|
1.6447738
|
1.1575969
|
1.2447261
|
1.3026216
|
1.2630766
|
1.3227959
|
1.3640579
|
1.3634306
|
1.3895943
|
1.3910433
|
1.4013556
|
1.4160392
|
1.4035249
|
2.194001
|
2.9530040
|
3.3859652
|
2.6620560
|
2.9488593
|
3.1349697
|
2.8452050
|
2.9530572
|
3.0325612
|
2.9167360
|
2.9570730
|
2.9908517
|
1.282673e+00
|
1.5206545
|
1.4229317
|
1.3809908
|
1.4082162
|
1.4251940
|
1.4167668
|
1.4330891
|
1.4325368
|
1.4267040
|
1.4439083
|
1.4253409
|
1.8423873
|
2.4863038
|
3.2129767
|
2.5352098
|
2.6448515
|
2.6991264
|
2.8165294
|
2.6127613
|
2.7216841
|
2.7222384
|
2.6995275
|
2.6989619
|
2.1053856
|
2.0948229
|
2.0747187
|
2.1651362
|
2.1873436
|
2.1930469
|
2.1826866
|
2.1993038
|
2.2044388
|
2.2261064
|
2.2532418
|
2.2204340
|
1.143407e+00
|
2.2319445
|
4.0112431
|
1.447822e-01
|
1.509765e+00
|
1.494126e+00
|
1.106289e+00
|
2.2140312
|
1.2356301
|
1.06777297
|
3.0917690
|
4.110406e+00
|
3.0800760
|
2.3915354
|
2.3939485
|
2.4090143
|
2.4395653
|
2.3694690
|
2.3644406
|
2.3630603
|
2.3654867
|
2.3960114
|
2.4235625
|
2.3890228
|
1.143407e+00
|
2.2319445
|
4.0112431
|
1.447822e-01
|
1.509765e+00
|
1.494126e+00
|
1.106289e+00
|
2.2140312
|
1.235630
|
1.06777297
|
3.091769
|
4.110406e+00
|
1.6903712
|
1.6845594
|
1.6856146
|
1.7226741
|
1.6956102
|
1.6633170
|
1.6492112
|
1.6421563
|
1.6287561
|
1.6316713
|
1.6350810
|
1.6004296
|
0.87646667
|
0.8764667
|
2.5648667
|
0.42466667
|
0.8764667
|
0.87646667
|
0.8764667
|
1.9194000
|
0.8764667
|
0.8764667
|
2.5808667
|
2.62086667
|
1.9014670
|
1.9276983
|
1.9066016
|
1.7710551
|
1.6984037
|
1.5825790
|
1.4851646
|
1.4667794
|
1.4124348
|
1.4209702
|
1.3300939
|
1.3199670
|
1.40707008
|
2.3886325
|
1.8354282
|
1.4125732
|
2.8722901
|
1.6088991
|
2.8731781
|
2.6827613
|
1.6022091
|
2.93727981
|
2.0625370
|
2.1521682
|
2
|
2.0
|
2
|
3
|
1
|
2
|
2.0
|
1.0
|
2.0
|
2
|
2
|
3.0
|
1
|
Same
|
1
|
Same
|
0
|
Same
|
ARI_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
AR_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
|
701000321
|
TAAKA VODKA 80 1L
|
701000321
|
9.021729e-04
|
1.759268e-02
|
not white noise
|
NA
|
0.01899300
|
0.10000000
|
stationary
|
1.1710043
|
1.2004915
|
1.2094658
|
1.0908761
|
0.9925427
|
0.9056197
|
0.8398687
|
0.7668376
|
0.7063319
|
0.6671581
|
0.6332420
|
0.6049786
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2294891
|
1.3192009
|
1.3555344
|
1.2380914
|
1.1219847
|
1.0132678
|
0.9271811
|
0.8370151
|
0.7745875
|
0.7366518
|
0.7016322
|
0.6697700
|
1.4136262
|
1.3258148
|
1.3652444
|
1.1735504
|
0.9831519
|
1.1211774
|
1.1476283
|
1.1635058
|
1.2537695
|
1.2941281
|
1.2613047
|
1.2517146
|
4.9505043
|
2.9344969
|
2.3457840
|
2.5624829
|
2.4150441
|
2.2656664
|
2.1414018
|
1.9553184
|
1.8213283
|
1.7053062
|
1.6001769
|
1.4984614
|
1.5723548
|
1.4633890
|
1.5054031
|
1.2272708
|
1.0921195
|
1.0500487
|
1.0002845
|
1.0361209
|
1.1136744
|
1.1867192
|
1.2578197
|
1.3051289
|
1.4342972
|
1.5853167
|
1.6797874
|
1.6693680
|
1.6072430
|
1.5589531
|
1.5251200
|
1.4546881
|
1.3651390
|
1.3021452
|
1.2429854
|
1.2040647
|
1.504535
|
1.4691917
|
1.6258440
|
1.5581537
|
1.5476994
|
1.5718885
|
1.5628395
|
1.5605300
|
1.5642155
|
1.5630380
|
1.5625864
|
1.5631408
|
1.322547e+00
|
1.7211078
|
1.8703293
|
1.9101641
|
1.8088129
|
1.6926424
|
1.6207221
|
1.5910010
|
1.5188421
|
1.4191297
|
1.3462616
|
1.3141439
|
1.2204117
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
0.6579638
|
0.5701615
|
0.7752091
|
0.8155160
|
0.8846794
|
0.9374149
|
0.9783549
|
0.9933732
|
1.0115258
|
1.0361741
|
1.0589252
|
1.0846531
|
2.000000e+00
|
2.0000000
|
4.0000000
|
-4.348374e-17
|
6.753857e-17
|
1.000000e+00
|
6.753857e-17
|
1.0000000
|
1.0000000
|
1.00000000
|
1.0000000
|
2.000000e+00
|
0.8263359
|
0.7231268
|
0.9128621
|
1.1243435
|
1.1876421
|
1.1582706
|
1.1786536
|
1.1696388
|
1.1729235
|
1.1849318
|
1.2007783
|
1.2167080
|
2.000000e+00
|
2.0000000
|
4.0000000
|
-2.405483e-17
|
-1.350771e-16
|
1.000000e+00
|
-9.344377e-17
|
1.0000000
|
1.000000
|
1.00000000
|
1.000000
|
2.000000e+00
|
0.4807360
|
0.5043518
|
0.5616449
|
0.5304247
|
0.5372993
|
0.5529296
|
0.5593234
|
0.5564176
|
0.5633958
|
0.5765286
|
0.5883527
|
0.6018623
|
1.90196667
|
1.9019667
|
2.2679667
|
0.41273333
|
0.4127333
|
1.04470000
|
0.4127333
|
1.0447000
|
1.0447000
|
1.0447000
|
1.0447000
|
1.90196667
|
3.9071392
|
2.5666005
|
2.2953243
|
1.9757884
|
1.6754489
|
1.4705390
|
1.3205687
|
1.1962381
|
1.0999955
|
1.0299462
|
0.9706569
|
0.9268957
|
2.92898406
|
1.1099905
|
0.7302893
|
0.4426791
|
1.0990789
|
1.0469681
|
1.0574663
|
1.0522111
|
1.0554095
|
1.07558045
|
1.0590376
|
1.0528707
|
2
|
2.0
|
1
|
1
|
2
|
2
|
1.0
|
1.0
|
1.0
|
0
|
1
|
1.0
|
2
|
Same
|
0
|
Same
|
3
|
Same
|
RF_1_ASE
|
RF_2_ASE
|
RF_3_ASE
|
RF_4_ASE
|
RF_5_ASE
|
RF_6_ASE
|
RF_7_ASE
|
RF_8_ASE
|
RF_9_ASE
|
RF_10_ASE
|
RF_11_ASE
|
RF_12_ASE
|
|
701001902
|
TAAKA VODKA 80 1L
|
701001902
|
0.000000e+00
|
1.110223e-16
|
not white noise
|
NA
|
0.36038955
|
0.01000000
|
not stationary
|
1.2988034
|
1.3129060
|
1.3201709
|
1.4180342
|
1.4875214
|
1.5586325
|
1.7266422
|
1.8734829
|
1.9894017
|
2.0936752
|
2.1824864
|
2.2449573
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.3539749
|
1.3588057
|
1.3633975
|
1.3813713
|
1.4473666
|
1.5173847
|
1.6858683
|
1.8330004
|
1.9517698
|
2.0594757
|
2.1512192
|
2.2159832
|
3.7100420
|
2.9400814
|
2.4805010
|
2.2061830
|
2.0424458
|
1.9447130
|
1.8863775
|
1.8515577
|
1.8307742
|
1.8183687
|
1.8109641
|
1.8065443
|
1.3417939
|
1.3484977
|
1.3506908
|
1.3635321
|
1.4317118
|
1.5017399
|
1.6695923
|
1.8108711
|
1.9283069
|
2.0358965
|
2.1287877
|
2.1928990
|
3.7100420
|
2.9400814
|
2.4805010
|
2.2061830
|
2.0424458
|
1.9447130
|
1.8863775
|
1.8515577
|
1.8307742
|
1.8183687
|
1.8109641
|
1.8065443
|
1.3619844
|
1.3979031
|
1.3598847
|
1.1737398
|
1.2521387
|
1.3339832
|
1.4364247
|
1.4530547
|
1.5484084
|
1.6936525
|
1.8040564
|
1.8510117
|
3.869573
|
3.6125856
|
3.3060967
|
3.7829621
|
3.7514210
|
3.7702081
|
3.6297962
|
3.6826525
|
3.6834965
|
3.7152756
|
3.6890658
|
3.6921358
|
1.367044e+00
|
1.4393103
|
1.4454570
|
1.2712999
|
1.3515299
|
1.4066517
|
1.4658585
|
1.5148079
|
1.6072195
|
1.7576524
|
1.8606205
|
1.9186453
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
2.0390568
|
1.9103108
|
1.8639272
|
1.7744949
|
1.7821436
|
1.8103704
|
1.8886985
|
1.9404900
|
1.9854733
|
2.0311932
|
2.0698215
|
2.1555864
|
2.624291e+00
|
3.4435520
|
3.0438653
|
2.816562e+00
|
1.619171e+00
|
2.475240e+00
|
2.363029e+00
|
3.2778318
|
1.2124732
|
2.16253670
|
3.1243225
|
5.095097e+00
|
2.1767938
|
1.9467462
|
1.9045347
|
1.7544638
|
1.7551391
|
1.7862844
|
1.8603817
|
1.9505741
|
1.9893415
|
2.0347064
|
2.0739335
|
2.1491457
|
2.624291e+00
|
3.4435520
|
3.0438653
|
2.816562e+00
|
1.619171e+00
|
2.475240e+00
|
2.363029e+00
|
3.2778318
|
1.212473
|
2.16253670
|
3.124322
|
5.095097e+00
|
1.8125036
|
1.7418683
|
1.6982769
|
1.7598982
|
1.8052501
|
1.8507820
|
1.9600512
|
2.0390245
|
2.0950955
|
2.1456867
|
2.1850600
|
2.2469619
|
1.08143333
|
1.9650000
|
1.9650000
|
1.96500000
|
1.0814333
|
1.96500000
|
1.9650000
|
2.4627333
|
1.0814333
|
1.9650000
|
2.4627333
|
4.14833333
|
2.8972838
|
3.0439758
|
3.1362305
|
3.3683880
|
3.5260839
|
3.6875473
|
3.9850734
|
4.2524345
|
4.4635931
|
4.6311881
|
4.7651206
|
4.8565443
|
0.89315424
|
0.8257569
|
0.7864354
|
0.7645685
|
0.7527375
|
0.7464319
|
0.7430978
|
0.7413422
|
0.7404197
|
0.73993538
|
0.7396812
|
0.7395478
|
2
|
2.0
|
2
|
4
|
2
|
3
|
5.0
|
3.0
|
3.0
|
1
|
2
|
2.0
|
1
|
Same
|
9
|
Different
|
5
|
Inconclusive
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
ARI_4_ASE
|
ARI_5_ASE
|
ARI_6_ASE
|
ARI_7_ASE
|
ARI_8_ASE
|
ARI_9_ASE
|
ARI_10_ASE
|
ARI_11_ASE
|
ARI_12_ASE
|
|
700004770
|
TAAKA VODKA 80 1L
|
700004770
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.67246321
|
0.01000000
|
not stationary
|
2.9373276
|
2.9334814
|
2.4566438
|
2.3000840
|
2.2445583
|
2.2117293
|
2.0643972
|
1.9509494
|
1.8610028
|
1.8277122
|
1.8067681
|
1.7906182
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.4184434
|
2.5123708
|
2.2574863
|
2.3310096
|
2.2647927
|
2.1165618
|
2.0792477
|
2.1082941
|
2.1459321
|
2.2500406
|
2.2233113
|
2.2709840
|
2.7090858
|
3.0045268
|
2.7552078
|
2.9158770
|
2.9878752
|
2.9840845
|
3.0388172
|
3.0739047
|
3.0963842
|
3.1251054
|
3.1481911
|
3.1676421
|
2.7372199
|
2.7608743
|
2.6294740
|
2.5938914
|
2.5092408
|
2.5661585
|
2.6474384
|
2.7557645
|
2.9071031
|
3.0625002
|
3.0725270
|
3.1218363
|
2.9293446
|
2.9539608
|
2.9770709
|
2.9987670
|
3.0191356
|
3.0382579
|
3.0562102
|
3.0730642
|
3.0888869
|
3.1037414
|
3.1176871
|
3.1307795
|
4.2135756
|
3.1578717
|
2.9261509
|
3.1086898
|
3.0513504
|
2.9361785
|
3.0445157
|
3.2151829
|
3.4272852
|
3.6682226
|
3.7203684
|
3.8690611
|
2.561615
|
2.8651879
|
2.5024834
|
2.6232964
|
2.6669439
|
2.6019462
|
2.6270449
|
2.6327923
|
2.6212863
|
2.6263640
|
2.6269996
|
2.6249889
|
3.295872e+00
|
2.7258590
|
2.6967588
|
2.7422302
|
2.7448767
|
2.7308850
|
2.8594171
|
3.0683988
|
3.2781511
|
3.5145773
|
3.5802386
|
3.7311803
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
12.8951641
|
13.8017956
|
14.0855938
|
13.9004389
|
13.9267486
|
14.1203957
|
14.2350064
|
14.3480930
|
14.1482276
|
13.9645015
|
13.1432818
|
12.4147606
|
2.104223e+00
|
5.6896429
|
3.1213717
|
2.401091e+00
|
3.595388e+00
|
5.725325e+00
|
3.814024e+00
|
2.8738998
|
2.9145646
|
1.94209150
|
3.9607583
|
1.973405e+00
|
15.3958816
|
15.8869049
|
15.9214910
|
15.3606947
|
15.1356711
|
14.9409582
|
14.6203682
|
14.6236395
|
14.4563891
|
14.2016280
|
13.3333187
|
12.5893127
|
1.306533e+00
|
4.7272362
|
2.0822285
|
1.381773e+00
|
2.634530e+00
|
4.847809e+00
|
3.027774e+00
|
2.1796301
|
2.307767
|
1.41588971
|
3.507124
|
1.584109e+00
|
8.6401205
|
8.3117593
|
7.7364838
|
7.6932788
|
7.7409568
|
7.9931846
|
8.1207489
|
8.1529607
|
8.0531443
|
7.9974316
|
7.9008112
|
7.8253273
|
5.56812000
|
7.4269400
|
5.5681200
|
5.07279000
|
5.5681200
|
6.51265667
|
5.5681200
|
5.0727900
|
5.0727900
|
4.5102900
|
5.5681200
|
4.51029000
|
4.3210093
|
4.4547752
|
5.1038760
|
5.8181472
|
6.4199667
|
7.1546660
|
7.7889282
|
8.4227042
|
8.9977760
|
9.5352287
|
10.0420725
|
10.5573231
|
5.44376396
|
5.4995514
|
5.9477890
|
5.9171998
|
6.1801249
|
6.1055649
|
5.9892702
|
6.2901612
|
6.3350567
|
5.98461479
|
6.4835015
|
6.2549101
|
4
|
4.0
|
3
|
1
|
2
|
2
|
4.0
|
2.0
|
3.0
|
1
|
2
|
5.0
|
2
|
Same
|
3
|
Same
|
0
|
Same
|
ARMA_1_ASE
|
AR_2_ASE
|
AR_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
AR_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
|
701000320
|
TAAKA VODKA 80 1L
|
701000320
|
7.397740e-02
|
1.020833e-01
|
white noise
|
white noise
|
0.02456406
|
0.10000000
|
stationary
|
0.3058120
|
0.2891453
|
0.2835897
|
0.2808120
|
0.2801709
|
0.2660684
|
0.2607570
|
0.2574145
|
0.2610826
|
0.2742735
|
0.2852991
|
0.2949145
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
0.4109945
|
0.3429946
|
0.3195829
|
0.3075331
|
0.3017865
|
0.2840345
|
0.2761569
|
0.2708897
|
0.2730606
|
0.2850537
|
0.2950993
|
0.3038980
|
1.1222686
|
1.1448731
|
1.1490522
|
1.1498248
|
1.1499676
|
1.1499940
|
1.1499989
|
1.1499998
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
0.4051081
|
0.3400124
|
0.3178584
|
0.3010784
|
0.3005917
|
0.2830615
|
0.2753256
|
0.2701542
|
0.2723935
|
0.2844376
|
0.2945231
|
0.3040786
|
1.1262931
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
0.5132315
|
0.4626041
|
0.4464509
|
0.4337997
|
0.4364314
|
0.4040055
|
0.3789017
|
0.3693601
|
0.3734112
|
0.3791848
|
0.3864571
|
0.3944063
|
1.000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
3.367507e-01
|
0.3334983
|
0.3298248
|
0.3271361
|
0.3369339
|
0.3258995
|
0.3179715
|
0.3165767
|
0.3249886
|
0.3380303
|
0.3485719
|
0.3592423
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.1340058
|
1.0679335
|
1.0091456
|
0.9661325
|
0.9585311
|
0.9523236
|
0.9479513
|
0.9351778
|
0.9252982
|
0.9250609
|
0.9178862
|
0.9119114
|
-2.858620e-01
|
0.9182829
|
0.9766402
|
9.933223e-01
|
-1.908896e-03
|
1.999454e+00
|
9.998440e-01
|
0.9999554
|
0.9999873
|
0.99999636
|
0.9999990
|
9.999997e-01
|
1.0951482
|
1.0391884
|
1.0373243
|
0.9956590
|
0.9750075
|
0.9688032
|
0.9617803
|
0.9495702
|
0.9384780
|
0.9410152
|
0.9327267
|
0.9238507
|
-2.835440e-01
|
1.0000000
|
1.0000000
|
1.000000e+00
|
0.000000e+00
|
2.000000e+00
|
1.000000e+00
|
1.0000000
|
1.000000
|
1.00000000
|
1.000000
|
1.000000e+00
|
0.6561855
|
0.6565808
|
0.7003539
|
0.7046057
|
0.7120659
|
0.7196140
|
0.7142612
|
0.6932445
|
0.6809154
|
0.6908256
|
0.6890456
|
0.6840134
|
0.38016667
|
1.0475667
|
1.0475667
|
1.05156667
|
0.3801667
|
1.79690000
|
1.0515667
|
1.0515667
|
1.0515667
|
1.0515667
|
1.0515667
|
1.05156667
|
1.2704633
|
0.7902063
|
0.9919788
|
1.7385973
|
1.4966013
|
1.2847326
|
1.1402432
|
1.0577520
|
1.0187777
|
0.9963401
|
0.9567935
|
0.9744953
|
0.01788975
|
1.0473739
|
0.1251189
|
-0.8943855
|
0.7900679
|
1.0613808
|
0.8380988
|
0.8704413
|
0.1268132
|
0.07903939
|
1.0291624
|
0.9396770
|
2
|
2.0
|
1
|
1
|
1
|
1
|
0.0
|
1.0
|
2.0
|
0
|
1
|
1.0
|
0
|
Same
|
0
|
Same
|
0
|
Same
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
|
700005926
|
TAAKA VODKA 80 1L
|
700005926
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.02043425
|
0.01000000
|
inconclusive
|
1.2773077
|
1.2785897
|
1.2738889
|
1.2734615
|
1.2670513
|
1.2888462
|
1.3058791
|
1.3186538
|
1.3055128
|
1.2732051
|
1.2467716
|
1.2067949
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5949354
|
1.6698877
|
1.5969432
|
1.6606368
|
1.5623477
|
1.5258236
|
1.4828369
|
1.4134233
|
1.3567679
|
1.2987954
|
1.2405394
|
1.1854457
|
2.0347432
|
1.8326340
|
1.7600970
|
1.6877429
|
1.6434318
|
1.6102652
|
1.5875657
|
1.5714169
|
1.5601218
|
1.5521628
|
1.5465727
|
1.5426409
|
1.4330655
|
1.5153141
|
1.4548217
|
1.5009097
|
1.3966455
|
1.3343924
|
1.2841664
|
1.2087831
|
1.1483447
|
1.0884805
|
1.0262071
|
0.9793421
|
2.2002900
|
2.1617869
|
2.1255065
|
2.0913205
|
2.0591081
|
2.0287553
|
2.0001548
|
1.9732053
|
1.9478117
|
1.9238840
|
1.9013376
|
1.8800928
|
1.4398552
|
1.5195100
|
1.5184085
|
1.5859954
|
1.5328043
|
1.4954347
|
1.4321918
|
1.3207035
|
1.2452052
|
1.1692806
|
1.1084649
|
1.0580159
|
2.033213
|
2.4407069
|
2.5035081
|
2.2105951
|
2.6555476
|
2.3640473
|
2.2586150
|
2.3888940
|
2.3980910
|
2.2768631
|
2.4599279
|
2.4002156
|
2.667315e+00
|
3.4118599
|
3.8792223
|
3.8945939
|
4.0508092
|
3.9659928
|
3.9156449
|
3.7875039
|
3.7042064
|
3.5755196
|
3.5150700
|
3.4031691
|
1.6954044
|
1.9238215
|
2.0300241
|
1.9299860
|
2.3085755
|
2.3687749
|
2.2688382
|
2.2545610
|
2.2051669
|
2.0706909
|
2.0700251
|
2.1066515
|
2.4843369
|
2.5933924
|
2.4217137
|
2.3937643
|
2.2534314
|
2.1727392
|
2.1260824
|
2.0622448
|
2.0125935
|
1.9728727
|
1.9403737
|
1.9068811
|
1.528962e+00
|
-0.1109384
|
1.9738719
|
-6.153660e-03
|
1.998551e+00
|
1.999659e+00
|
1.999920e+00
|
2.9999811
|
2.9999955
|
1.99999895
|
2.9999998
|
2.000000e+00
|
9.7833663
|
7.4145723
|
5.6429707
|
4.8216838
|
4.4154853
|
4.1634533
|
3.9056714
|
3.6334260
|
3.4048642
|
3.2365527
|
3.0866317
|
2.9483866
|
1.683491e+00
|
0.0158938
|
2.4608878
|
5.416634e-01
|
2.256431e+00
|
1.826491e+00
|
1.551416e+00
|
2.6000376
|
2.910352
|
2.25189453
|
3.394532
|
2.261570e+00
|
1.7369855
|
1.6840632
|
1.4945164
|
1.4199458
|
1.3165819
|
1.2587791
|
1.2325094
|
1.2087996
|
1.1944730
|
1.1628796
|
1.1468996
|
1.1314584
|
2.66716667
|
1.2049667
|
2.6671667
|
1.20496667
|
2.6671667
|
2.66716667
|
2.6671667
|
3.4851667
|
3.4851667
|
2.6671667
|
3.4851667
|
2.66716667
|
1.9815283
|
1.4925119
|
1.7653872
|
1.7753093
|
1.5579850
|
1.5285820
|
1.5997674
|
1.6019603
|
1.6406658
|
1.7676871
|
1.8581245
|
1.9469936
|
2.99735071
|
2.2192355
|
3.2957532
|
3.0317952
|
2.6326761
|
3.1494350
|
3.2783227
|
3.1141100
|
3.2517128
|
3.47956373
|
3.4682128
|
3.5185595
|
3
|
2.0
|
1
|
2
|
1
|
3
|
1.0
|
2.0
|
2.0
|
2
|
2
|
2.0
|
7
|
Inconclusive
|
7
|
Inconclusive
|
0
|
Same
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
RF_6_ASE
|
RF_7_ASE
|
ARMA_8_ASE
|
ARMA_9_ASE
|
ARMA_10_ASE
|
ARMA_11_ASE
|
ARMA_12_ASE
|
|
701001800
|
TAAKA VODKA 80 1L
|
701001800
|
1.224362e-01
|
4.792812e-01
|
white noise
|
white noise
|
0.21045181
|
0.03920430
|
not stationary
|
0.3269444
|
0.3769444
|
0.3799359
|
0.4064316
|
0.4248932
|
0.4372009
|
0.4547833
|
0.4801496
|
0.4956054
|
0.5087393
|
0.5194852
|
0.5196795
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.3326459
|
0.3795082
|
0.3816483
|
0.4077157
|
0.4259204
|
0.4380569
|
0.4555170
|
0.4807916
|
0.4961761
|
0.5092529
|
0.5199522
|
0.5201075
|
0.8399643
|
0.8335971
|
0.8333438
|
0.8333338
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.3269444
|
0.3769444
|
0.3799359
|
0.4064316
|
0.4248932
|
0.4372009
|
0.4547833
|
0.4801496
|
0.4956054
|
0.5087393
|
0.5194852
|
0.5196795
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.3038506
|
0.3294627
|
0.3613460
|
0.3897791
|
0.4590059
|
0.5096649
|
0.5446374
|
0.5727522
|
0.5942989
|
0.6217336
|
0.6417869
|
0.6553897
|
1.288629
|
1.1688163
|
1.5155480
|
1.1853348
|
1.3199271
|
1.2424943
|
1.3672014
|
1.2536331
|
1.3115650
|
1.2734871
|
1.3195815
|
1.2791524
|
3.715443e-01
|
0.4106859
|
0.4270984
|
0.4572844
|
0.4870052
|
0.5077215
|
0.5265536
|
0.5514860
|
0.5634607
|
0.5780082
|
0.5876334
|
0.5895793
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
1.3949814
|
1.5311940
|
1.5271148
|
1.5442603
|
1.5491202
|
1.5720521
|
1.5635692
|
1.5807947
|
1.6058078
|
1.6189796
|
1.6267983
|
1.6036859
|
8.789558e-01
|
-0.2864555
|
1.7378374
|
3.338108e-01
|
1.039229e+00
|
-1.243127e-01
|
1.210245e+00
|
0.2435835
|
1.0993840
|
0.97692315
|
2.0546778
|
1.121891e+00
|
1.7195772
|
1.7795639
|
1.6700771
|
1.6177478
|
1.5964515
|
1.5903798
|
1.5545462
|
1.5487558
|
1.5511204
|
1.5446621
|
1.5448047
|
1.5176862
|
8.789558e-01
|
-0.2864555
|
1.7378374
|
3.338108e-01
|
1.039229e+00
|
-1.243127e-01
|
1.210245e+00
|
0.2435835
|
1.099384
|
0.97692315
|
2.054678
|
1.121891e+00
|
0.9505822
|
1.1025279
|
1.1268384
|
1.2035475
|
1.2010364
|
1.2188499
|
1.1941809
|
1.2073098
|
1.2196685
|
1.2171446
|
1.2267873
|
1.2089110
|
0.99710000
|
0.1344000
|
0.9971000
|
0.13440000
|
1.0091000
|
0.13440000
|
1.0091000
|
0.1344000
|
1.0091000
|
1.0091000
|
1.7836667
|
1.00910000
|
0.3406550
|
0.4047964
|
0.4265396
|
0.4701055
|
0.5021653
|
0.5246189
|
0.5593738
|
0.5994740
|
0.6311114
|
0.6533767
|
0.6769308
|
0.6822033
|
0.67600436
|
0.6760044
|
0.6333375
|
0.6333375
|
0.5907647
|
0.5907647
|
0.5781798
|
0.5781798
|
0.6049114
|
0.60491143
|
0.6087298
|
0.6087298
|
1
|
2.0
|
1
|
2
|
0
|
1
|
2.0
|
2.0
|
1.0
|
1
|
1
|
1.0
|
0
|
Same
|
2
|
Same
|
0
|
Same
|
ARI_1_ASE
|
ARI_2_ASE
|
ARI_3_ASE
|
ARI_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
|
700003059
|
TAAKA VODKA 80 1L
|
700003059
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.01000000
|
0.01000000
|
inconclusive
|
1.3594872
|
1.3876923
|
1.3475214
|
1.3319231
|
1.3246154
|
1.2406838
|
1.1990476
|
1.1681410
|
1.1401140
|
1.1015385
|
1.0662471
|
1.0265812
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7845721
|
0.8710630
|
0.8805775
|
0.9141666
|
0.9391085
|
0.8890611
|
0.8853890
|
0.8725017
|
0.8647016
|
0.8382883
|
0.8151311
|
0.7857451
|
1.0481323
|
1.1368026
|
0.6405396
|
0.8426175
|
0.8769147
|
0.7516900
|
0.7846040
|
0.7922664
|
0.7587759
|
0.7609552
|
0.7608364
|
0.7507220
|
0.7822079
|
0.8905432
|
0.8879031
|
0.9104011
|
0.9222210
|
0.8704409
|
0.8534507
|
0.8295435
|
0.8112416
|
0.7765995
|
0.7437519
|
0.7113119
|
0.9216603
|
1.5009765
|
0.9665898
|
1.4078036
|
0.9958728
|
1.3314352
|
1.0134655
|
1.2682290
|
1.0223817
|
1.2153779
|
1.0249141
|
1.1707156
|
0.8020909
|
0.9021547
|
0.9007357
|
0.9361987
|
0.9587411
|
0.9153007
|
0.9283680
|
0.9170694
|
0.9204754
|
0.9018550
|
0.8799246
|
0.8522108
|
1.274858
|
1.3430106
|
0.7381308
|
1.0936850
|
1.1334287
|
0.9515117
|
1.0499681
|
1.0673239
|
1.0129014
|
1.0399485
|
1.0466752
|
1.0304761
|
2.591532e+00
|
2.7108124
|
2.4617483
|
2.3576535
|
2.2807949
|
2.1356822
|
2.1222404
|
2.1004201
|
2.0960634
|
2.0576792
|
2.0084829
|
1.9710193
|
1.2748582
|
1.3430106
|
0.7381308
|
1.0936850
|
1.1334287
|
0.9515117
|
1.0499681
|
1.0673239
|
1.0129014
|
1.0399485
|
1.0466752
|
1.0304761
|
1.7689070
|
1.8089697
|
1.7124797
|
1.6846435
|
1.7404499
|
1.7295824
|
1.7236226
|
1.7213262
|
1.6967922
|
1.6308052
|
1.5753490
|
1.5213369
|
-2.765066e-01
|
1.3644443
|
0.4884796
|
9.041992e-01
|
3.579109e+00
|
1.527870e+00
|
1.258682e+00
|
2.1668142
|
1.9137674
|
1.70682274
|
1.7917767
|
-9.828707e-02
|
1.7414562
|
1.8158592
|
1.7079300
|
1.6763549
|
1.7339938
|
1.7245794
|
1.7257332
|
1.7230273
|
1.6985303
|
1.6307441
|
1.5755449
|
1.5202460
|
-2.765066e-01
|
1.3644443
|
0.4884796
|
9.041992e-01
|
3.579109e+00
|
1.527870e+00
|
1.258682e+00
|
2.1668142
|
1.913767
|
1.70682274
|
1.791777
|
-9.828707e-02
|
1.1508434
|
1.1633860
|
1.1678729
|
1.1500508
|
1.1258950
|
1.0834738
|
1.0771968
|
1.0664143
|
1.0386723
|
0.9968171
|
0.9602420
|
0.9226749
|
0.94816667
|
1.4943000
|
0.9481667
|
0.94816667
|
1.4943000
|
0.94816667
|
0.9481667
|
1.4943000
|
1.4943000
|
1.4943000
|
1.4943000
|
0.10476667
|
0.6892937
|
0.7051572
|
0.7148292
|
0.7031495
|
0.7005994
|
0.6859124
|
0.6800124
|
0.6599456
|
0.6417744
|
0.6262822
|
0.6047723
|
0.6313556
|
0.98301281
|
1.3249443
|
1.3984097
|
1.1881379
|
1.7765832
|
1.4074940
|
1.3367883
|
1.5545717
|
1.5028699
|
1.55556295
|
1.5704580
|
1.6449353
|
1
|
0.0
|
1
|
1
|
1
|
1
|
2.0
|
1.0
|
2.0
|
1
|
2
|
0.0
|
10
|
Different
|
8
|
Inconclusive
|
2
|
Same
|
MLP_1_ASE
|
MLP_2_ASE
|
MLP_3_ASE
|
MLP_4_ASE
|
MLP_5_ASE
|
MLP_6_ASE
|
MLP_7_ASE
|
MLP_8_ASE
|
MLP_9_ASE
|
MLP_10_ASE
|
MLP_11_ASE
|
MLP_12_ASE
|
|
701000360
|
TAAKA VODKA 80 1L
|
701000360
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.01782149
|
0.01000000
|
inconclusive
|
1.0547703
|
1.1156677
|
1.1445139
|
1.1615011
|
1.1998985
|
1.2030609
|
1.2641110
|
1.2986806
|
1.3393857
|
1.4147703
|
1.4755162
|
1.5616079
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.6719714
|
0.6300118
|
0.7577797
|
0.8145712
|
0.8591158
|
0.8942577
|
0.9422469
|
1.0086066
|
1.0556249
|
1.1380708
|
1.2053652
|
1.2938916
|
1.2880147
|
1.9607699
|
1.5563160
|
1.5176694
|
1.4476984
|
1.3413498
|
1.2904318
|
1.2191043
|
1.1667074
|
1.1166898
|
1.0722279
|
1.0332015
|
0.7409955
|
0.6622150
|
0.7679869
|
0.7996367
|
0.8200111
|
0.8356037
|
0.8565329
|
0.9073472
|
0.9353860
|
1.0023231
|
1.0571398
|
1.1326307
|
1.2880147
|
1.9607699
|
1.5563160
|
1.5176694
|
1.4476984
|
1.3413498
|
1.2904318
|
1.2191043
|
1.1667074
|
1.1166898
|
1.0722279
|
1.0332015
|
0.6897048
|
0.6538503
|
0.8242495
|
0.8849206
|
0.8868103
|
0.9181049
|
0.8907489
|
0.9387022
|
0.9217718
|
0.9294319
|
0.9334659
|
0.9461155
|
1.548404
|
2.2453039
|
2.0482803
|
2.0237832
|
2.1001411
|
2.0408910
|
2.0708135
|
2.0614928
|
2.0611865
|
2.0640606
|
2.0616581
|
2.0629336
|
6.671504e-01
|
0.6490383
|
0.7694516
|
0.8198088
|
0.8097014
|
0.8220716
|
0.8006063
|
0.8420725
|
0.8396961
|
0.8555306
|
0.8745281
|
0.9032365
|
1.5484043
|
2.2453039
|
2.0482803
|
2.0237832
|
2.1001411
|
2.0408910
|
2.0708135
|
2.0614928
|
2.0611865
|
2.0640606
|
2.0616581
|
2.0629336
|
0.8956003
|
0.9114742
|
0.9405928
|
0.9510732
|
0.9486377
|
0.9452439
|
0.9426024
|
0.9405613
|
0.9391290
|
0.9379581
|
0.9367209
|
0.9356515
|
8.040531e-01
|
1.5864517
|
0.8869675
|
1.774077e-01
|
1.950560e+00
|
5.516148e-02
|
1.980573e+00
|
1.0175486
|
0.9927707
|
2.00568554
|
0.9973925
|
3.001868e+00
|
0.8000180
|
0.7884722
|
0.8333405
|
0.8557746
|
0.8692351
|
0.8782087
|
0.8846184
|
0.8894258
|
0.8931648
|
0.8961560
|
0.8986033
|
0.9006428
|
1.280019e+00
|
1.6965226
|
1.0000000
|
0.000000e+00
|
2.000000e+00
|
0.000000e+00
|
2.000000e+00
|
1.0000000
|
1.000000
|
2.00000000
|
1.000000
|
3.000000e+00
|
0.7727211
|
0.7641832
|
0.7474693
|
0.7538572
|
0.7617306
|
0.7896883
|
0.7917159
|
0.8077206
|
0.8131983
|
0.8099480
|
0.8110756
|
0.8077618
|
1.06510000
|
1.0651000
|
1.0651000
|
0.31305000
|
1.9605333
|
0.31305000
|
1.9605333
|
1.0651000
|
1.0651000
|
1.9605333
|
1.0651000
|
2.01853333
|
1.5863008
|
1.2902889
|
2.7559933
|
2.9194146
|
2.7037700
|
3.5078072
|
3.6148993
|
3.6625561
|
4.3911635
|
4.6099363
|
4.6512789
|
5.3085430
|
2.34632999
|
2.0462247
|
3.6948482
|
3.1425098
|
2.8818331
|
4.0920569
|
3.4967159
|
3.5282732
|
4.3132387
|
4.15708159
|
4.0560629
|
5.0389953
|
1
|
2.0
|
1
|
1
|
2
|
1
|
3.0
|
0.0
|
2.0
|
3
|
2
|
3.0
|
8
|
Inconclusive
|
12
|
Different
|
13
|
Different
|
ARIMA_1_ASE
|
AR_2_ASE
|
RF_3_ASE
|
RF_4_ASE
|
RF_5_ASE
|
RF_6_ASE
|
RF_7_ASE
|
RF_8_ASE
|
RF_9_ASE
|
RF_10_ASE
|
RF_11_ASE
|
RF_12_ASE
|
|
700004711
|
TAAKA VODKA 80 1L
|
700004711
|
1.225766e-01
|
5.601297e-01
|
white noise
|
white noise
|
0.34105678
|
0.01000000
|
not stationary
|
0.2550041
|
0.2575682
|
0.2569699
|
0.2547476
|
0.2537733
|
0.2535511
|
0.2547476
|
0.2746515
|
0.3067420
|
0.3466964
|
0.3926031
|
0.4436152
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.2639497
|
0.2614047
|
0.2595438
|
0.2566789
|
0.2553180
|
0.2548383
|
0.2558510
|
0.2756169
|
0.3076001
|
0.3474687
|
0.3933052
|
0.4442588
|
0.5563444
|
0.5136196
|
0.5169007
|
0.5166487
|
0.5166680
|
0.5166666
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.2550041
|
0.2575682
|
0.2569699
|
0.2547476
|
0.2537733
|
0.2535511
|
0.2547476
|
0.2746515
|
0.3067420
|
0.3466964
|
0.3926031
|
0.4436152
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.3436862
|
0.3307649
|
0.3149455
|
0.2949122
|
0.2707834
|
0.2572632
|
0.2636435
|
0.2862654
|
0.3083257
|
0.3313031
|
0.3534166
|
0.3804094
|
1.041703
|
0.8336566
|
0.8137993
|
0.7729034
|
0.7951749
|
0.6027690
|
0.8833233
|
0.7696072
|
0.7743422
|
0.7612159
|
0.7764075
|
0.7366452
|
3.361097e-01
|
0.3155127
|
0.2919478
|
0.2764727
|
0.2586763
|
0.2496937
|
0.2544425
|
0.2763915
|
0.3005871
|
0.3288234
|
0.3599117
|
0.3946049
|
0.8174024
|
0.6424489
|
0.6798952
|
0.6718803
|
0.6735958
|
0.6732286
|
0.6733072
|
0.6732904
|
0.6732940
|
0.6732932
|
0.6732934
|
0.6732934
|
0.5112069
|
0.4870015
|
0.4387405
|
0.4291552
|
0.4133043
|
0.4031663
|
0.3986917
|
0.3969075
|
0.3960754
|
0.3952592
|
0.4009168
|
0.4313326
|
5.736152e-17
|
1.0000000
|
1.0000000
|
5.736152e-17
|
1.000000e+00
|
7.817820e-17
|
1.000000e+00
|
1.0000000
|
1.0000000
|
1.00000000
|
1.0000000
|
1.128727e-16
|
0.5959641
|
0.5948793
|
0.4870828
|
0.4477523
|
0.4126828
|
0.4140054
|
0.4148010
|
0.4167965
|
0.4173774
|
0.4165885
|
0.4219115
|
0.4476560
|
5.736152e-17
|
1.0000000
|
1.0000000
|
5.736152e-17
|
1.000000e+00
|
7.817820e-17
|
1.000000e+00
|
1.0000000
|
1.000000
|
1.00000000
|
1.000000
|
1.128727e-16
|
0.3542367
|
0.3420991
|
0.3165973
|
0.3192477
|
0.3219040
|
0.3260338
|
0.3306873
|
0.3362587
|
0.3402844
|
0.3437473
|
0.3572899
|
0.3936388
|
0.05796667
|
0.9420667
|
0.9420667
|
0.05796667
|
0.9420667
|
0.05796667
|
0.9420667
|
0.9420667
|
0.9420667
|
0.9420667
|
0.9420667
|
0.05796667
|
0.2396020
|
0.2711712
|
0.2460723
|
0.2519696
|
0.2617462
|
0.2525414
|
0.2816764
|
0.2890390
|
0.3329149
|
0.3493588
|
0.3844444
|
0.3942741
|
0.49803989
|
0.4253242
|
0.7629265
|
0.5603698
|
1.1433358
|
0.6928211
|
1.3045775
|
0.9762882
|
1.6646413
|
1.11870466
|
1.8288005
|
1.3980319
|
0
|
1.0
|
1
|
1
|
1
|
0
|
0.0
|
2.0
|
2.0
|
2
|
2
|
2.0
|
0
|
Same
|
5
|
Inconclusive
|
5
|
Inconclusive
|
MLP_1_ASE
|
EqualMeans_2_ASE
|
MLP_3_ASE
|
MLP_4_ASE
|
EqualMeans_5_ASE
|
ARIMA_6_ASE
|
ARIMA_7_ASE
|
EqualMeans_8_ASE
|
ARIMA_9_ASE
|
ARIMA_10_ASE
|
ARI_11_ASE
|
ARI_12_ASE
|
aggregated_forecast = results[c("Product_Type","Product","Customer","winning_12","ACTUAL_1","ACTUAL_2","ACTUAL_3","ACTUAL_4","ACTUAL_5","ACTUAL_6","ACTUAL_7","ACTUAL_8","ACTUAL_9","ACTUAL_10","ACTUAL_11","ACTUAL_12")]
for (i in 1:z){
if (results$winning_12[i] == "EqualMeans_12_ASE"){
aggregated_forecast$F1[i] <- results$EqualMeans_F1[i]
aggregated_forecast$F2[i] <- results$EqualMeans_F2[i]
aggregated_forecast$F3[i] <- results$EqualMeans_F3[i]
aggregated_forecast$F4[i] <- results$EqualMeans_F4[i]
aggregated_forecast$F5[i] <- results$EqualMeans_F5[i]
aggregated_forecast$F6[i] <- results$EqualMeans_F6[i]
aggregated_forecast$F7[i] <- results$EqualMeans_F7[i]
aggregated_forecast$F8[i] <- results$EqualMeans_F8[i]
aggregated_forecast$F9[i] <- results$EqualMeans_F9[i]
aggregated_forecast$F10[i] <- results$EqualMeans_F10[i]
aggregated_forecast$F11[i] <- results$EqualMeans_F11[i]
aggregated_forecast$F12[i] <- results$EqualMeans_F12[i]
} else if (results$winning_12[i] == "AR_12_ASE"){
aggregated_forecast$F1[i] <- results$AR_F1[i]
aggregated_forecast$F2[i] <- results$AR_F2[i]
aggregated_forecast$F3[i] <- results$AR_F3[i]
aggregated_forecast$F4[i] <- results$AR_F4[i]
aggregated_forecast$F5[i] <- results$AR_F5[i]
aggregated_forecast$F6[i] <- results$AR_F6[i]
aggregated_forecast$F7[i] <- results$AR_F7[i]
aggregated_forecast$F8[i] <- results$AR_F8[i]
aggregated_forecast$F9[i] <- results$AR_F9[i]
aggregated_forecast$F10[i] <- results$AR_F10[i]
aggregated_forecast$F11[i] <- results$AR_F11[i]
aggregated_forecast$F12[i] <- results$AR_F12[i]
} else if (results$winning_12[i] == "ARMA_12_ASE"){
aggregated_forecast$F1[i] <- results$ARMA_F1[i]
aggregated_forecast$F2[i] <- results$ARMA_F2[i]
aggregated_forecast$F3[i] <- results$ARMA_F3[i]
aggregated_forecast$F4[i] <- results$ARMA_F4[i]
aggregated_forecast$F5[i] <- results$ARMA_F5[i]
aggregated_forecast$F6[i] <- results$ARMA_F6[i]
aggregated_forecast$F7[i] <- results$ARMA_F7[i]
aggregated_forecast$F8[i] <- results$ARMA_F8[i]
aggregated_forecast$F9[i] <- results$ARMA_F9[i]
aggregated_forecast$F10[i] <- results$ARMA_F10[i]
aggregated_forecast$F11[i] <- results$ARMA_F11[i]
aggregated_forecast$F12[i] <- results$ARMA_F12[i]
} else if (results$winning_12[i] == "ARI_12_ASE"){
aggregated_forecast$F1[i] <- results$ARI_F1[i]
aggregated_forecast$F2[i] <- results$ARI_F2[i]
aggregated_forecast$F3[i] <- results$ARI_F3[i]
aggregated_forecast$F4[i] <- results$ARI_F4[i]
aggregated_forecast$F5[i] <- results$ARI_F5[i]
aggregated_forecast$F6[i] <- results$ARI_F6[i]
aggregated_forecast$F7[i] <- results$ARI_F7[i]
aggregated_forecast$F8[i] <- results$ARI_F8[i]
aggregated_forecast$F9[i] <- results$ARI_F9[i]
aggregated_forecast$F10[i] <- results$ARI_F10[i]
aggregated_forecast$F11[i] <- results$ARI_F11[i]
aggregated_forecast$F12[i] <- results$ARI_F12[i]
} else if (results$winning_12[i] == "ARIMA_12_ASE"){
aggregated_forecast$F1[i] <- results$ARIMA_F1[i]
aggregated_forecast$F2[i] <- results$ARIMA_F2[i]
aggregated_forecast$F3[i] <- results$ARIMA_F3[i]
aggregated_forecast$F4[i] <- results$ARIMA_F4[i]
aggregated_forecast$F5[i] <- results$ARIMA_F5[i]
aggregated_forecast$F6[i] <- results$ARIMA_F6[i]
aggregated_forecast$F7[i] <- results$ARIMA_F7[i]
aggregated_forecast$F8[i] <- results$ARIMA_F8[i]
aggregated_forecast$F9[i] <- results$ARIMA_F9[i]
aggregated_forecast$F10[i] <- results$ARIMA_F10[i]
aggregated_forecast$F11[i] <- results$ARIMA_F11[i]
aggregated_forecast$F12[i] <- results$ARIMA_F12[i]
} else if (results$winning_12[i] == "ARI_S12_12_ASE"){
aggregated_forecast$F1[i] <- results$ARI_S12_F1[i]
aggregated_forecast$F2[i] <- results$ARI_S12_F2[i]
aggregated_forecast$F3[i] <- results$ARI_S12_F3[i]
aggregated_forecast$F4[i] <- results$ARI_S12_F4[i]
aggregated_forecast$F5[i] <- results$ARI_S12_F5[i]
aggregated_forecast$F6[i] <- results$ARI_S12_F6[i]
aggregated_forecast$F7[i] <- results$ARI_S12_F7[i]
aggregated_forecast$F8[i] <- results$ARI_S12_F8[i]
aggregated_forecast$F9[i] <- results$ARI_S12_F9[i]
aggregated_forecast$F10[i] <- results$ARI_S12_F10[i]
aggregated_forecast$F11[i] <- results$ARI_S12_F11[i]
aggregated_forecast$F12[i] <- results$ARI_S12_F12[i]
} else if (results$winning_12[i] == "ARIMA_S12_12_ASE"){
aggregated_forecast$F1[i] <- results$ARIMA_S12_F1[i]
aggregated_forecast$F2[i] <- results$ARIMA_S12_F2[i]
aggregated_forecast$F3[i] <- results$ARIMA_S12_F3[i]
aggregated_forecast$F4[i] <- results$ARIMA_S12_F4[i]
aggregated_forecast$F5[i] <- results$ARIMA_S12_F5[i]
aggregated_forecast$F6[i] <- results$ARIMA_S12_F6[i]
aggregated_forecast$F7[i] <- results$ARIMA_S12_F7[i]
aggregated_forecast$F8[i] <- results$ARIMA_S12_F8[i]
aggregated_forecast$F9[i] <- results$ARIMA_S12_F9[i]
aggregated_forecast$F10[i] <- results$ARIMA_S12_F10[i]
aggregated_forecast$F11[i] <- results$ARIMA_S12_F11[i]
aggregated_forecast$F12[i] <- results$ARIMA_S12_F12[i]
} else if (results$winning_12[i] == "RF_12_ASE"){
aggregated_forecast$F1[i] <- results$RF_F1[i]
aggregated_forecast$F2[i] <- results$RF_F2[i]
aggregated_forecast$F3[i] <- results$RF_F3[i]
aggregated_forecast$F4[i] <- results$RF_F4[i]
aggregated_forecast$F5[i] <- results$RF_F5[i]
aggregated_forecast$F6[i] <- results$RF_F6[i]
aggregated_forecast$F7[i] <- results$RF_F7[i]
aggregated_forecast$F8[i] <- results$RF_F8[i]
aggregated_forecast$F9[i] <- results$RF_F9[i]
aggregated_forecast$F10[i] <- results$RF_F10[i]
aggregated_forecast$F11[i] <- results$RF_F11[i]
aggregated_forecast$F12[i] <- results$RF_F12[i]
} else if (results$winning_12[i] == "MLP_12_ASE"){
aggregated_forecast$F1[i] <- results$MLP_F1[i]
aggregated_forecast$F2[i] <- results$MLP_F2[i]
aggregated_forecast$F3[i] <- results$MLP_F3[i]
aggregated_forecast$F4[i] <- results$MLP_F4[i]
aggregated_forecast$F5[i] <- results$MLP_F5[i]
aggregated_forecast$F6[i] <- results$MLP_F6[i]
aggregated_forecast$F7[i] <- results$MLP_F7[i]
aggregated_forecast$F8[i] <- results$MLP_F8[i]
aggregated_forecast$F9[i] <- results$MLP_F9[i]
aggregated_forecast$F10[i] <- results$MLP_F10[i]
aggregated_forecast$F11[i] <- results$MLP_F11[i]
aggregated_forecast$F12[i] <- results$MLP_F12[i]
}
}
# other time series that did not have enough data points, will use mean for forecasts
zz = nrow(combinations_mean)
results_mean <- data.frame(Product_Type=integer(),
Product=character(),
Customer=integer(),
EqualMeans_F1=double(),
EqualMeans_F2=double(),
EqualMeans_F3=double(),
EqualMeans_F4=double(),
EqualMeans_F5=double(),
EqualMeans_F6=double(),
EqualMeans_F7=double(),
EqualMeans_F8=double(),
EqualMeans_F9=double(),
EqualMeans_F10=double(),
EqualMeans_F11=double(),
EqualMeans_F12=double(),
ACTUAL_1=double(),
ACTUAL_2=double(),
ACTUAL_3=double(),
ACTUAL_4=double(),
ACTUAL_5=double(),
ACTUAL_6=double(),
ACTUAL_7=double(),
ACTUAL_8=double(),
ACTUAL_9=double(),
ACTUAL_10=double(),
ACTUAL_11=double(),
ACTUAL_12=double(),
stringsAsFactors = FALSE)
# loop through sample combinations
for(i in 1:zz) {
sample_combinations1 = combinations_mean[i,]
temp1 = inner_join(temp_mean,sample_combinations1)
product = sample_combinations1$Product
customer = sample_combinations1$Customer_ID
product_type = sample_combinations1$Customer_ID
results_mean[i,"Product_Type"] = product_type
results_mean[i,"Product"] = as.character(sample_combinations1$Product)
results_mean[i,"Customer"] = customer
par(mfrow=c(1,1))
plot.ts(temp1$STD_Cases,
main=c(paste("Standard Case Sales of ", product),
paste("for Customer",customer)),
xlab="Months",
ylab="Standard Cases")
j=12
#Equal Means Model
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model0_mean = mean(temp1$STD_Cases[k:(k+(trainingSize-1))])
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - model0_mean)^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - model0_mean)^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - model0_mean)^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - model0_mean)^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - model0_mean)^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - model0_mean)^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - model0_mean)^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - model0_mean)^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - model0_mean)^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - model0_mean)^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - model0_mean)^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - model0_mean)^2)
sink()
assign(paste("EqualMeans_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - model0_mean)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results_mean[i,paste0("EqualMeans_1_ASE")] = WindowedASE1
results_mean[i,paste0("EqualMeans_2_ASE")] = WindowedASE2
results_mean[i,paste0("EqualMeans_3_ASE")] = WindowedASE3
results_mean[i,paste0("EqualMeans_4_ASE")] = WindowedASE4
results_mean[i,paste0("EqualMeans_5_ASE")] = WindowedASE5
results_mean[i,paste0("EqualMeans_6_ASE")] = WindowedASE6
results_mean[i,paste0("EqualMeans_7_ASE")] = WindowedASE7
results_mean[i,paste0("EqualMeans_8_ASE")] = WindowedASE8
results_mean[i,paste0("EqualMeans_9_ASE")] = WindowedASE9
results_mean[i,paste0("EqualMeans_10_ASE")] = WindowedASE10
results_mean[i,paste0("EqualMeans_11_ASE")] = WindowedASE11
results_mean[i,paste0("EqualMeans_12_ASE")] = WindowedASE12
results_mean[i,paste0("EqualMeans_F1")] = model0_mean
results_mean[i,paste0("EqualMeans_F2")] = model0_mean
results_mean[i,paste0("EqualMeans_F3")] = model0_mean
results_mean[i,paste0("EqualMeans_F4")] = model0_mean
results_mean[i,paste0("EqualMeans_F5")] = model0_mean
results_mean[i,paste0("EqualMeans_F6")] = model0_mean
results_mean[i,paste0("EqualMeans_F7")] = model0_mean
results_mean[i,paste0("EqualMeans_F8")] = model0_mean
results_mean[i,paste0("EqualMeans_F9")] = model0_mean
results_mean[i,paste0("EqualMeans_F10")] = model0_mean
results_mean[i,paste0("EqualMeans_F11")] = model0_mean
results_mean[i,paste0("EqualMeans_F12")] = model0_mean
results_mean[i,paste0("ACTUAL_1")] = temp1$STD_Cases[73]
results_mean[i,paste0("ACTUAL_2")] = temp1$STD_Cases[74]
results_mean[i,paste0("ACTUAL_3")] = temp1$STD_Cases[75]
results_mean[i,paste0("ACTUAL_4")] = temp1$STD_Cases[76]
results_mean[i,paste0("ACTUAL_5")] = temp1$STD_Cases[77]
results_mean[i,paste0("ACTUAL_6")] = temp1$STD_Cases[78]
results_mean[i,paste0("ACTUAL_7")] = temp1$STD_Cases[79]
results_mean[i,paste0("ACTUAL_8")] = temp1$STD_Cases[80]
results_mean[i,paste0("ACTUAL_9")] = temp1$STD_Cases[81]
results_mean[i,paste0("ACTUAL_10")] = temp1$STD_Cases[82]
results_mean[i,paste0("ACTUAL_11")] = temp1$STD_Cases[83]
results_mean[i,paste0("ACTUAL_12")] = temp1$STD_Cases[84]
}




|
Product_Type
|
Product
|
Customer
|
EqualMeans_F1
|
EqualMeans_F2
|
EqualMeans_F3
|
EqualMeans_F4
|
EqualMeans_F5
|
EqualMeans_F6
|
EqualMeans_F7
|
EqualMeans_F8
|
EqualMeans_F9
|
EqualMeans_F10
|
EqualMeans_F11
|
EqualMeans_F12
|
ACTUAL_1
|
ACTUAL_2
|
ACTUAL_3
|
ACTUAL_4
|
ACTUAL_5
|
ACTUAL_6
|
ACTUAL_7
|
ACTUAL_8
|
ACTUAL_9
|
ACTUAL_10
|
ACTUAL_11
|
ACTUAL_12
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
winning_12
|
|
700005895
|
TAAKA VODKA 80 1L
|
700005895
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
1
|
2
|
0
|
2
|
2
|
0
|
2
|
2
|
2
|
2
|
2
|
2
|
0.4156197
|
0.4976709
|
0.5207479
|
0.5726709
|
0.6376709
|
0.6788675
|
0.7321032
|
0.7938248
|
0.8606339
|
0.9310043
|
1.0048970
|
1.0839957
|
EqualMeans_12_ASE
|
|
701000357
|
TAAKA VODKA 80 1L
|
701000357
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0.3264744
|
0.3328846
|
0.3350214
|
0.3367308
|
0.3403205
|
0.3444231
|
0.3473535
|
0.3495513
|
0.3512607
|
0.3526282
|
0.3537471
|
0.3546795
|
EqualMeans_12_ASE
|
|
700005867
|
TAAKA VODKA 80 1L
|
700005867
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
1
|
1
|
0
|
0
|
0
|
0
|
1
|
0
|
0
|
0
|
0
|
1
|
0.2278045
|
0.2489583
|
0.2401976
|
0.2358173
|
0.2237019
|
0.2160524
|
0.2173649
|
0.2183494
|
0.2135595
|
0.2099840
|
0.2072917
|
0.2090011
|
EqualMeans_12_ASE
|
|
701001908
|
TAAKA VODKA 80 1L
|
701001908
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
EqualMeans_12_ASE
|
aggregated_forecast_mean = results_mean[c("Product_Type","Product","Customer","winning_12","ACTUAL_1","ACTUAL_2","ACTUAL_3","ACTUAL_4","ACTUAL_5","ACTUAL_6","ACTUAL_7","ACTUAL_8","ACTUAL_9","ACTUAL_10","ACTUAL_11","ACTUAL_12")]
aggregated_forecast_mean$F1 <- results_mean$EqualMeans_F1
aggregated_forecast_mean$F2 <- results_mean$EqualMeans_F2
aggregated_forecast_mean$F3 <- results_mean$EqualMeans_F3
aggregated_forecast_mean$F4 <- results_mean$EqualMeans_F4
aggregated_forecast_mean$F5 <- results_mean$EqualMeans_F5
aggregated_forecast_mean$F6 <- results_mean$EqualMeans_F6
aggregated_forecast_mean$F7 <- results_mean$EqualMeans_F7
aggregated_forecast_mean$F8 <- results_mean$EqualMeans_F8
aggregated_forecast_mean$F9 <- results_mean$EqualMeans_F9
aggregated_forecast_mean$F10 <- results_mean$EqualMeans_F10
aggregated_forecast_mean$F11 <- results_mean$EqualMeans_F11
aggregated_forecast_mean$F12 <- results_mean$EqualMeans_F12
combined_aggregated_forecast <- rbind(aggregated_forecast, aggregated_forecast_mean)
combined_actuals_forecasts <- combined_aggregated_forecast %>% summarize_if(is.numeric,sum,na.rm=TRUE)
actuals <- combined_actuals_forecasts[c("ACTUAL_1","ACTUAL_2","ACTUAL_3","ACTUAL_4","ACTUAL_5","ACTUAL_6","ACTUAL_7","ACTUAL_8","ACTUAL_9","ACTUAL_10","ACTUAL_11","ACTUAL_12")]
actuals_trans <- transpose(actuals)
forecasts <- combined_actuals_forecasts[c("F1","F2","F3","F4","F5","F6","F7","F8","F9","F10","F11","F12")]
forecasts_trans <- transpose(forecasts)